머신러닝 가이드
1.0.0
참고: 이 편리한 확장 Markdown PDF를 사용하면 VSCode에서 이 마크다운 파일을 PDF로 쉽게 변환할 수 있습니다.
기계 학습/딥 러닝 프레임워크.
ML을 위한 학습 리소스
ML 프레임워크, 라이브러리 및 도구
알고리즘
PyTorch 개발
TensorFlow 개발
핵심 ML 개발
딥러닝 개발
강화 학습 개발
컴퓨터 비전 개발
자연어 처리(NLP) 개발
생물정보학
CUDA 개발
MATLAB 개발
C/C++ 개발
자바 개발
파이썬 개발
스칼라 개발
R 개발
줄리아 개발
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머신 러닝은 프로그래밍할 필요 없이 데이터 모델에서 학습하고 시간이 지남에 따라 정확성을 향상시키는 알고리즘을 사용하여 앱을 구축하는 데 중점을 둔 인공 지능(AI)의 한 분야입니다.
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Microsoft의 자연어 처리(NLP) 모범 사례
Microsoft의 자율주행 요리책
Azure 기계 학습 - 서비스로서의 ML | 마이크로소프트 애저
Azure Machine Learning 작업 영역에서 Jupyter Notebook을 실행하는 방법
기계 학습 및 인공 지능 | 아마존 웹 서비스
Amazon SageMaker 임시 인스턴스에서 Jupyter 노트북 예약
AI 및 머신러닝 | 구글 클라우드
Google Cloud에서 Apache Spark와 함께 Jupyter Notebook 사용
머신러닝 | 애플 개발자
인공 지능 및 자동 조종 장치 | 테슬라
메타 AI 도구 | 페이스북
PyTorch 튜토리얼
TensorFlow 튜토리얼
JupyterLab
Apple Silicon 기반 Core ML을 통한 안정적인 확산
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Andrew Ng의 스탠포드 대학교 기계 학습 | 코세라
기계 학습(ML) 과정에 대한 AWS 교육 및 인증
Microsoft Azure용 기계 학습 장학금 프로그램 | 유다시티
Microsoft 인증: Azure 데이터 과학자 준회원
Microsoft 인증: Azure AI 엔지니어 어소시에이트
Azure Machine Learning 교육 및 배포
Google Cloud 교육을 통해 머신러닝 및 인공지능 학습
Google Cloud 머신러닝 단기집중과정
온라인 머신러닝 강좌 | 유데미
온라인 머신러닝 강좌 | 코세라
온라인 강좌 및 수업을 통해 기계 학습 배우기 | edX
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기계 학습 소개(PDF)
인공 지능: 현대적인 접근 방식 - Stuart J. Russel 및 Peter Norvig
Ian Goodfellow, Yoshoua Bengio 및 Aaron Courville의 딥 러닝
Andriy Burkov가 쓴 백 페이지짜리 기계 학습 책
Tom M. Mitchell의 기계 학습
집단지성 프로그래밍: 스마트 웹 2.0 애플리케이션 구축 - Toby Segaran
기계 학습: 알고리즘 관점, 제2판
패턴 인식 및 기계 학습 - Christopher M. Bishop
Python을 사용한 자연어 처리 - Steven Bird, Ewan Klein 및 Edward Loper
Python 기계 학습: 초보자를 위한 기계 학습에 대한 기술적 접근 방식 작성자: Leonard Eddison
베이지안 추론과 기계 학습 - David Barber
완전 초보자를 위한 기계 학습: Oliver Theobald의 쉬운 영어 소개
Ben Wilson의 실제 머신 러닝
Scikit-Learn, Keras 및 TensorFlow를 사용한 실습 기계 학습: 지능형 시스템 구축을 위한 개념, 도구 및 기술 작성자: Aurélien Géron
Python을 사용한 기계 학습 소개: Andreas C. Müller 및 Sarah Guido의 데이터 과학자를 위한 가이드
해커를 위한 기계 학습: Drew Conway 및 John Myles White가 시작하는 데 도움이 되는 사례 연구 및 알고리즘
통계 학습의 요소: 데이터 마이닝, 추론 및 예측 - Trevor Hastie, Robert Tibshirani 및 Jerome Friedman
분산 기계 학습 패턴 - 도서(온라인에서 무료로 읽을 수 있음) + 코드
실제 기계 학습 [무료 장]
통계 학습 소개 - 책 + R 코드
통계 학습의 요소 - 도서
Think Bayes - 책 + Python 코드
대규모 데이터세트 마이닝
머신러닝과의 첫 만남
기계 학습 소개 - Alex Smola 및 SVN Vishwanathan
패턴 인식의 확률론적 이론
정보 검색 소개
예측: 원칙과 실천
기계 학습 소개 - Amnon Shashua
강화 학습
기계 학습
AI에 대한 탐구
데이터 과학을 위한 R 프로그래밍
데이터 마이닝 - 실용적인 기계 학습 도구 및 기술
TensorFlow를 사용한 머신러닝
기계 학습 시스템
기계 학습의 기초 - Mehryar Mohri, Afshin Rostamizadeh 및 Ameet Talwalkar
AI 기반 검색 - Trey Grainger, Doug Turnbull, Max Irwin -
기계 학습을 위한 앙상블 방법 - Gautam Kunapuli
실제 머신러닝 엔지니어링 - Ben Wilson
개인 정보 보호 기계 학습 - J. Morris Chang, Di Zhuang, G. Dumindu Samaraweera
자동화된 기계 학습 실행 - Qingquan Song, Haifeng Jin 및 Xia Hu
분산 기계 학습 패턴 - Yuan Tang
기계 학습 프로젝트 관리: 설계부터 배포까지 - Simon Thompson
인과적 기계 학습 - Robert Ness
베이지안 최적화 실행 - Quan Nguyen
심층적인 기계 학습 알고리즘) - Vadim Smolyakov
최적화 알고리즘 - Alaa Khamis
Guillaume Saupin의 실용적인 그라디언트 부스팅
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TensorFlow는 머신러닝을 위한 엔드투엔드 오픈소스 플랫폼입니다. 연구자가 ML의 최첨단 기능을 활용하고 개발자가 ML 기반 애플리케이션을 쉽게 구축 및 배포할 수 있는 도구, 라이브러리, 커뮤니티 리소스로 구성된 포괄적이고 유연한 생태계를 갖추고 있습니다.
Keras는 Python으로 작성되었으며 TensorFlow, CNTK 또는 Theano 위에서 실행될 수 있는 고급 신경망 API입니다. 빠른 실험을 가능하게 하는 데 중점을 두고 개발되었습니다. TensorFlow, Microsoft Cognitive Toolkit, R, Theano 또는 PlaidML 위에서 실행될 수 있습니다.
PyTorch는 그래프, 포인트 클라우드, 매니폴드 등 불규칙한 입력 데이터에 대한 딥러닝을 위한 라이브러리입니다. 주로 Facebook의 AI 연구소에서 개발되었습니다.
Amazon SageMaker는 모든 개발자와 데이터 과학자에게 기계 학습(ML) 모델을 신속하게 구축, 교육 및 배포할 수 있는 기능을 제공하는 완전관리형 서비스입니다. SageMaker는 기계 학습 프로세스의 각 단계에서 어려운 작업을 제거하여 고품질 모델을 더 쉽게 개발할 수 있도록 해줍니다.
Azure Databricks는 데이터 과학 및 데이터 엔지니어링을 위해 설계된 빠르고 협업적인 Apache Spark 기반 빅 데이터 분석 서비스입니다. Azure Databricks는 몇 분 만에 Apache Spark 환경을 설정하고 대화형 작업 영역에서 공유 프로젝트에 대해 자동 크기 조정 및 공동 작업을 수행합니다. Azure Databricks는 Python, Scala, R, Java 및 SQL뿐만 아니라 TensorFlow, PyTorch 및 scikit-learn을 포함한 데이터 과학 프레임워크 및 라이브러리도 지원합니다.
CNTK(Microsoft Cognitive Toolkit)는 상용급 분산 딥 러닝을 위한 오픈 소스 도구 키트입니다. 이는 방향성 그래프를 통해 신경망을 일련의 계산 단계로 설명합니다. CNTK를 사용하면 사용자는 피드포워드 DNN, CNN(컨볼루션 신경망), RNN/LSTM(반복 신경망)과 같은 널리 사용되는 모델 유형을 쉽게 실현하고 결합할 수 있습니다. CNTK는 여러 GPU 및 서버에 걸쳐 자동 미분 및 병렬화를 통해 확률적 경사하강법(SGD, 오류 역전파) 학습을 구현합니다.
Apple CoreML은 기계 학습 모델을 앱에 통합하는 데 도움이 되는 프레임워크입니다. Core ML은 모든 모델에 대한 통합 표현을 제공합니다. 앱은 Core ML API와 사용자 데이터를 사용하여 사용자 기기에서 예측을 수행하고 모델을 교육하거나 미세 조정합니다. 모델은 훈련 데이터 세트에 기계 학습 알고리즘을 적용한 결과입니다. 모델을 사용하여 새로운 입력 데이터를 기반으로 예측을 합니다.
Apache OpenNLP는 자연어 텍스트 처리에 사용되는 기계 학습 기반 툴킷용 오픈 소스 라이브러리입니다. 명명된 엔터티 인식, 문장 감지, POS(품사) 태깅, 토큰화 기능 추출, 청킹, 구문 분석 및 상호 참조 해결과 같은 사용 사례를 위한 API를 제공합니다.
Apache Airflow는 워크플로를 프로그래밍 방식으로 작성, 예약 및 모니터링하기 위해 커뮤니티에서 만든 오픈 소스 워크플로 관리 플랫폼입니다. 설치하다. 원칙. 확장 가능. Airflow는 모듈식 아키텍처를 가지며 메시지 대기열을 사용하여 임의 수의 작업자를 조정합니다. 공기 흐름은 무한대로 확장될 준비가 되어 있습니다.
ONNX(Open Neural Network Exchange)는 AI 개발자가 프로젝트가 진행됨에 따라 올바른 도구를 선택할 수 있도록 지원하는 개방형 생태계입니다. ONNX는 딥 러닝과 기존 ML 모두 AI 모델을 위한 오픈 소스 형식을 제공합니다. 이는 확장 가능한 계산 그래프 모델뿐만 아니라 내장 연산자 및 표준 데이터 유형의 정의도 정의합니다.
Apache MXNet은 효율성과 유연성을 모두 고려하여 설계된 딥 러닝 프레임워크입니다. 이를 통해 기호 프로그래밍과 명령형 프로그래밍을 혼합하여 효율성과 생산성을 극대화할 수 있습니다. MXNet의 핵심에는 기호 작업과 명령형 작업을 즉시 자동으로 병렬화하는 동적 종속성 스케줄러가 포함되어 있습니다. 그 위에 있는 그래프 최적화 레이어는 기호 실행을 빠르고 메모리 효율적으로 만듭니다. MXNet은 휴대성이 뛰어나고 가벼우며 여러 GPU 및 여러 머신으로 효과적으로 확장됩니다. Python, R, Julia, Scala, Go, Javascript 등을 지원합니다.
AutoGluon은 애플리케이션에서 강력한 예측 성능을 쉽게 달성할 수 있도록 기계 학습 작업을 자동화하는 딥 러닝용 툴킷입니다. 단 몇 줄의 코드만으로 표 형식, 이미지 및 텍스트 데이터에 대한 정확도가 높은 딥 러닝 모델을 훈련하고 배포할 수 있습니다.
Anaconda는 사용자가 모델을 개발하고 훈련하고 배포할 수 있는 기계 학습 및 딥 러닝을 위한 매우 인기 있는 데이터 과학 플랫폼입니다.
PlaidML은 랩톱, 임베디드 장치 또는 사용 가능한 컴퓨팅 하드웨어가 제대로 지원되지 않거나 사용 가능한 소프트웨어 스택에 불쾌한 라이선스 제한이 있는 기타 장치에서 딥 러닝을 활성화하기 위한 고급 휴대용 텐서 컴파일러입니다.
OpenCV는 실시간 컴퓨터 비전 애플리케이션에 중점을 두고 고도로 최적화된 라이브러리입니다. C++, Python 및 Java 인터페이스는 Linux, MacOS, Windows, iOS 및 Android를 지원합니다.
Scikit-Learn은 SciPy, NumPy 및 matplotlib를 기반으로 구축된 기계 학습용 Python 모듈로, 널리 사용되는 많은 기계 학습 알고리즘의 강력하고 간단한 구현을 더 쉽게 적용할 수 있습니다.
Weka는 그래픽 사용자 인터페이스, 표준 터미널 애플리케이션 또는 Java API를 통해 액세스할 수 있는 오픈 소스 기계 학습 소프트웨어입니다. 교육, 연구 및 산업 응용 분야에 널리 사용되며 표준 기계 학습 작업을 위한 다양한 내장 도구가 포함되어 있으며 추가적으로 scikit-learn, R 및 Deeplearning4j와 같은 잘 알려진 도구 상자에 대한 투명한 액세스를 제공합니다.
Caffe는 표현, 속도 및 모듈성을 염두에 두고 만들어진 딥 러닝 프레임워크입니다. 이는 BAIR(Berkeley AI Research)/BVLC(Berkeley Vision and Learning Center) 및 커뮤니티 기여자들에 의해 개발되었습니다.
Theano는 NumPy와의 긴밀한 통합을 포함하여 다차원 배열과 관련된 수학적 표현식을 효율적으로 정의, 최적화 및 평가할 수 있는 Python 라이브러리입니다.
nGraph는 딥러닝을 위한 오픈 소스 C++ 라이브러리, 컴파일러 및 런타임입니다. nGraph 컴파일러는 딥 러닝 프레임워크를 사용하여 AI 워크로드 개발을 가속화하고 다양한 하드웨어 대상에 배포하는 것을 목표로 합니다. 이는 AI 개발자에게 자유로움, 성능 및 사용 편의성을 제공합니다.
NVIDIA cuDNN은 심층 신경망을 위한 GPU 가속 기본 요소 라이브러리입니다. cuDNN은 순방향 및 역방향 컨볼루션, 풀링, 정규화 및 활성화 레이어와 같은 표준 루틴에 대해 고도로 조정된 구현을 제공합니다. cuDNN은 Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch 및 TensorFlow를 포함하여 널리 사용되는 딥 러닝 프레임워크를 가속화합니다.
Huginn은 온라인에서 자동화된 작업을 수행하는 에이전트를 구축하기 위한 자체 호스팅 시스템입니다. 웹을 읽고, 이벤트를 감시하고, 사용자를 대신하여 조치를 취할 수 있습니다. Huginn의 에이전트는 이벤트를 생성하고 소비하여 방향성 그래프를 따라 전파합니다. 자체 서버에 있는 IFTTT 또는 Zapier의 해킹 가능한 버전이라고 생각하세요.
Netron은 신경망, 딥 러닝, 머신 러닝 모델을 위한 뷰어입니다. ONNX, TensorFlow Lite, Caffe, Keras, Darknet, PaddlePaddle, ncnn, MNN, Core ML, RKNN, MXNet, MindSpore Lite, TNN, Barracuda, Tengine, CNTK, TensorFlow.js, Caffe2 및 UFF를 지원합니다.
도파민은 강화 학습 알고리즘의 빠른 프로토타이핑을 위한 연구 프레임워크입니다.
DALI는 고도로 최적화된 빌딩 블록과 딥 러닝 훈련 및 추론 애플리케이션을 가속화하기 위한 데이터 처리용 실행 엔진을 포함하는 GPU 가속 라이브러리입니다.
MindSpore Lite는 모바일, 엣지 및 클라우드 시나리오에 사용할 수 있는 새로운 오픈 소스 딥 러닝 교육/추론 프레임워크입니다.
Darknet은 C 및 CUDA로 작성된 오픈 소스 신경망 프레임워크입니다. 빠르고 설치가 쉬우며 CPU 및 GPU 계산을 지원합니다.
PaddlePaddle은 사용하기 쉽고 효율적이며 유연하고 확장 가능한 딥 러닝 플랫폼으로, 원래 Baidu의 많은 제품에 딥 러닝을 적용할 목적으로 Baidu 과학자 및 엔지니어가 개발했습니다.
GoogleNotebookLM은 중요한 통찰력을 더 빠르게 얻기 위해 기존 콘텐츠와 결합된 언어 모델의 힘을 사용하는 실험적인 AI 도구입니다. 사실을 요약하고, 복잡한 아이디어를 설명하고, 선택한 소스를 기반으로 새로운 연결을 브레인스토밍할 수 있는 가상 연구 조교와 유사합니다.
Unilm은 작업, 언어 및 양식에 걸친 대규모 자체 감독 사전 교육입니다.
SK(Semantic Kernel)는 AI LLM(대형 언어 모델)을 기존 프로그래밍 언어와 통합할 수 있는 경량 SDK입니다. SK 확장 가능 프로그래밍 모델은 자연어 의미론적 기능, 기존 코드 기본 기능, 임베딩 기반 메모리를 결합하여 AI를 통해 새로운 잠재력을 발휘하고 애플리케이션에 가치를 추가합니다.
Pandas AI는 생성 인공 지능 기능을 Pandas에 통합하여 데이터 프레임을 대화형으로 만드는 Python 라이브러리입니다.
NCNN은 모바일 플랫폼에 최적화된 고성능 신경망 추론 프레임워크입니다.
MNN은 매우 빠르고 가벼운 딥 러닝 프레임워크로, Alibaba의 비즈니스 핵심 사용 사례를 통해 실전 테스트를 거쳤습니다.
MediaPipe는 다양한 플랫폼에서 엔드투엔드 성능을 발휘하도록 최적화되어 있습니다. 데모 보기 자세히 알아보기 복잡한 온디바이스 ML, 단순화 우리는 온디바이스 ML을 사용자 정의 가능하고, 프로덕션에 바로 사용할 수 있으며, 플랫폼 전반에 걸쳐 액세스할 수 있게 만드는 복잡성을 추상화했습니다.
MegEngine은 훈련과 추론을 모두 위한 통합 프레임워크라는 3가지 주요 기능을 갖춘 빠르고 확장 가능하며 사용자 친화적인 딥 러닝 프레임워크입니다.
ML.NET은 다른 인기 있는 ML 프레임워크(TensorFlow, ONNX, Infer.NET 등)를 사용하고 이미지 분류와 같은 훨씬 더 많은 기계 학습 시나리오에 액세스할 수 있도록 확장 가능한 플랫폼으로 설계된 기계 학습 라이브러리입니다. 물체 감지 등.
Ludwig는 간단하고 유연한 데이터 기반 구성 시스템을 사용하여 기계 학습 파이프라인을 쉽게 정의할 수 있게 해주는 선언적 기계 학습 프레임워크입니다.
MMdnn은 딥 러닝(DL) 모델을 변환, 시각화 및 진단하는 포괄적인 크로스 프레임워크 도구입니다. "MM"은 모델 관리(Model Management)를 의미하고 "dnn"은 심층 신경망(Deep Neural Network)의 약어입니다. Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx 및 CoreML 간에 모델을 변환합니다.
Horovod는 TensorFlow, Keras, PyTorch 및 Apache MXNet을 위한 분산형 딥 러닝 교육 프레임워크입니다.
Vaex는 큰 표 형식의 데이터 세트를 시각화하고 탐색하기 위한 게으른 Out-of-Core DataFrame(Pandas와 유사)을 위한 고성능 Python 라이브러리입니다.
GluonTS는 PyTorch 및 MXNet을 기반으로 하는 딥 러닝 기반 모델에 중점을 둔 확률적 시계열 모델링을 위한 Python 패키지입니다.
MindsDB는 SQL을 사용하여 가장 강력한 데이터베이스 및 데이터 웨어하우스에 대한 기계 학습 워크플로를 지원하는 ML-SQL 서버입니다.
Jupyter Notebook은 라이브 코드, 방정식, 시각화 및 설명 텍스트가 포함된 문서를 만들고 공유할 수 있는 오픈 소스 웹 애플리케이션입니다. Jupyter는 데이터 정리 및 변환, 수치 시뮬레이션, 통계 모델링, 데이터 시각화, 데이터 과학 및 기계 학습을 수행하는 산업에서 널리 사용됩니다.
Apache Spark는 대규모 데이터 처리를 위한 통합 분석 엔진입니다. Scala, Java, Python, R의 고급 API와 데이터 분석을 위한 일반 계산 그래프를 지원하는 최적화된 엔진을 제공합니다. 또한 SQL 및 DataFrames용 Spark SQL, 기계 학습용 MLlib, 그래프 처리용 GraphX, 스트림 처리용 구조적 스트리밍을 비롯한 다양한 고급 도구 세트를 지원합니다.
SQL Server 및 Azure SQL용 Apache Spark 커넥터는 빅 데이터 분석에서 트랜잭션 데이터를 사용하고 임시 쿼리 또는 보고에 대한 결과를 유지할 수 있게 해주는 고성능 커넥터입니다. 커넥터를 사용하면 온프레미스 또는 클라우드의 모든 SQL 데이터베이스를 Spark 작업의 입력 데이터 원본 또는 출력 데이터 싱크로 사용할 수 있습니다.
Apache PredictionIO는 개발자, 데이터 과학자 및 최종 사용자를 위한 오픈 소스 기계 학습 프레임워크입니다. REST API를 통해 이벤트 수집, 알고리즘 배포, 평가, 예측 결과 쿼리를 지원합니다. Hadoop, HBase(및 기타 DB), Elasticsearch, Spark와 같은 확장 가능한 오픈 소스 서비스를 기반으로 하며 Lambda 아키텍처를 구현합니다.
Apache Kafka용 클러스터 관리자(CMAK)는 Apache Kafka 클러스터를 관리하기 위한 도구입니다.
BigDL은 Apache Spark용 분산 딥러닝 라이브러리입니다. BigDL을 사용하면 사용자는 기존 Spark 또는 Hadoop 클러스터 위에서 직접 실행할 수 있는 표준 Spark 프로그램으로 딥 러닝 애플리케이션을 작성할 수 있습니다.
Eclipse Deeplearning4J(DL4J)는 JVM 기반(Scala, Kotlin, Clojure 및 Groovy) 딥 러닝 애플리케이션의 모든 요구 사항을 지원하기 위한 프로젝트 세트입니다. 이는 원시 데이터로 시작하여 어디서든 어떤 형식이든 로드하고 전처리하여 다양한 단순 및 복잡한 딥 러닝 네트워크를 구축하고 조정하는 것을 의미합니다.
Tensorman은 System76에서 개발한 Tensorflow 컨테이너를 쉽게 관리하기 위한 유틸리티입니다. Tensorman을 사용하면 Tensorflow가 시스템의 나머지 부분과 격리된 환경에서 작동할 수 있습니다. 이 가상 환경은 기본 시스템과 독립적으로 작동할 수 있으므로 Docker 런타임을 지원하는 모든 버전의 Linux 배포판에서 모든 버전의 Tensorflow를 사용할 수 있습니다.
Numba는 Anaconda, Inc.가 후원하는 Python용 오픈 소스 NumPy 인식 최적화 컴파일러입니다. Numba는 LLVM 컴파일러 프로젝트를 사용하여 Python 구문에서 기계어 코드를 생성합니다. Numba는 많은 NumPy 함수를 포함하여 숫자 중심 Python의 대규모 하위 집합을 컴파일할 수 있습니다. 또한 Numba는 루프 자동 병렬화, GPU 가속 코드 생성, ufunc 및 C 콜백 생성을 지원합니다.
Chainer는 유연성을 목표로 하는 Python 기반의 딥러닝 프레임워크입니다. 실행별 정의 접근 방식(동적 계산 그래프)을 기반으로 하는 자동 차별화 API와 신경망을 구축하고 훈련하기 위한 객체 지향 고급 API를 제공합니다. 또한 고성능 훈련 및 추론을 위해 CuPy를 사용하는 CUDA/cuDNN을 지원합니다.
XGBoost는 매우 효율적이고 유연하며 이식 가능하도록 설계된 최적화된 분산 그래디언트 부스팅 라이브러리입니다. Gradient Boosting 프레임워크에서 기계 학습 알고리즘을 구현합니다. XGBoost는 빠르고 정확한 방법으로 많은 데이터 과학 문제를 해결하는 병렬 트리 부스팅(GBDT, GBM이라고도 함)을 제공합니다. AWS, GCE, Azure 및 Yarn 클러스터를 포함한 여러 시스템에 대한 분산 교육을 지원합니다. 또한 Flink, Spark 및 기타 클라우드 데이터 흐름 시스템과 통합될 수 있습니다.
cuML은 다른 RAPIDS 프로젝트와 호환되는 API를 공유하는 기계 학습 알고리즘과 수학적 기본 함수를 구현하는 라이브러리 모음입니다. cuML을 사용하면 데이터 과학자, 연구원 및 소프트웨어 엔지니어가 CUDA 프로그래밍의 세부 사항을 다루지 않고도 GPU에서 기존의 테이블 형식 ML 작업을 실행할 수 있습니다. 대부분의 경우 cuML의 Python API는 scikit-learn의 API와 일치합니다.
Emu는 이식성, 모듈성 및 성능에 중점을 둔 Rust용 GPGPU 라이브러리입니다. 이는 WebGPU를 CUDA처럼 느끼게 하는 특정 기능을 제공하는 WebGPU에 대한 CUDA와 같은 컴퓨팅 특정 추상화입니다.
Scalene은 다른 Python 프로파일러가 하지 않거나 할 수 없는 여러 가지 작업을 수행하는 Python용 고성능 CPU, GPU 및 메모리 프로파일러입니다. 훨씬 더 자세한 정보를 제공하면서 다른 많은 프로파일러보다 훨씬 빠르게 실행됩니다.
MLpack은 C++로 작성되고 Armadillo 선형 대수 라이브러리, 축소 수치 최적화 라이브러리 및 Boost의 일부를 기반으로 구축된 빠르고 유연한 C++ 기계 학습 라이브러리입니다.
Netron은 신경망, 딥 러닝, 머신 러닝 모델을 위한 뷰어입니다. ONNX, TensorFlow Lite, Caffe, Keras, Darknet, PaddlePaddle, ncnn, MNN, Core ML, RKNN, MXNet, MindSpore Lite, TNN, Barracuda, Tengine, CNTK, TensorFlow.js, Caffe2 및 UFF를 지원합니다.
Lightning은 DIY 인프라, 비용 관리, 확장 등을 처리하지 않고도 PyTorch 모델을 구축 및 교육하고 Lightning 앱 템플릿을 사용하여 ML 수명 주기에 연결하는 도구입니다.
OpenNN은 기계 학습을 위한 오픈 소스 신경망 라이브러리입니다. 여기에는 많은 인공 지능 솔루션을 처리하기 위한 정교한 알고리즘과 유틸리티가 포함되어 있습니다.
H20은 복잡한 비즈니스 문제를 해결하고 이해하고 신뢰할 수 있는 결과로 새로운 아이디어의 발견을 가속화하는 AI 클라우드 플랫폼입니다.
Gensim은 주제 모델링, 문서 색인화 및 대규모 말뭉치와의 유사성 검색을 위한 Python 라이브러리입니다. 대상 독자는 자연어 처리(NLP) 및 정보 검색(IR) 커뮤니티입니다.
llama.cpp는 C/C++로 된 Facebook의 LLaMA 모델 포트입니다.
hmmlearn은 비지도 학습 및 Hidden Markov 모델 추론을 위한 알고리즘 세트입니다.
Nextjournal은 재현 가능한 연구를 위한 노트북입니다. Docker 컨테이너에 넣을 수 있는 모든 것을 실행합니다. 다중 언어 노트북, 자동 버전 관리 및 실시간 협업을 통해 작업 흐름을 개선하세요. GPU 지원을 포함한 주문형 프로비저닝으로 시간과 비용을 절약하세요.
IPython은 다음을 통해 대화형 컴퓨팅을 위한 풍부한 아키텍처를 제공합니다.
Veles는 현재 삼성이 개발한 신속한 딥러닝 애플리케이션 개발을 위한 분산 플랫폼입니다.
DyNet은 Carnegie Mellon University 및 기타 여러 대학에서 개발한 신경망 라이브러리입니다. 이는 C++(Python 바인딩 포함)로 작성되었으며 CPU 또는 GPU에서 실행될 때 효율적이고 모든 교육 인스턴스에 대해 변경되는 동적 구조가 있는 네트워크에서 잘 작동하도록 설계되었습니다. 이러한 종류의 네트워크는 자연어 처리 작업에서 특히 중요하며 DyNet은 구문 분석, 기계 번역, 형태학적 활용 및 기타 여러 응용 분야를 위한 최첨단 시스템을 구축하는 데 사용되었습니다.
Ray는 AI 및 Python 애플리케이션 확장을 위한 통합 프레임워크입니다. ML 워크로드를 가속화하기 위한 핵심 분산 런타임과 라이브러리 툴킷(Ray AIR)으로 구성됩니다.
Whisper.cpp는 OpenAI의 Whisper 자동 음성 인식(ASR) 모델의 고성능 추론입니다.
ChatGPT Plus는 귀하와 채팅하고, 후속 질문에 답하고, 잘못된 가정에 도전할 수 있는 대화형 AI인 ChatGPT를 위한 파일럿 구독 플랜( 월 $20 )입니다.
Auto-GPT는 자연어로 목표를 제시하고 이를 하위 작업으로 나누고 자동 루프에서 인터넷 및 기타 도구를 사용하여 목표를 달성하려고 시도할 수 있는 "AI 에이전트"입니다. OpenAI의 GPT-4 또는 GPT-3.5 API를 사용하며 자율 작업을 수행하기 위해 GPT-4를 사용하는 애플리케이션의 첫 번째 예 중 하나입니다.
mckaywrigley의 Chatbot UI는 Next.js, TypeScript 및 Tailwind CSS를 사용하여 Chatbot UI Lite를 기반으로 구축된 OpenAI의 채팅 모델을 위한 고급 챗봇 키트입니다. 이 버전의 ChatBot UI는 GPT-3.5 및 GPT-4 모델을 모두 지원합니다. 대화는 브라우저 내에 로컬로 저장됩니다. 데이터 손실을 방지하기 위해 대화를 내보내고 가져올 수 있습니다. 데모를 확인하세요.
mckaywrigley의 Chatbot UI Lite는 Next.js, TypeScript 및 Tailwind CSS를 사용하는 OpenAI의 채팅 모델을 위한 간단한 챗봇 스타터 키트입니다. 데모를 확인하세요.
MiniGPT-4는 고급 대형 언어 모델을 통한 향상된 비전 언어 이해입니다.
GPT4All은 LLaMa를 기반으로 한 코드, 스토리 및 대화를 포함한 방대한 양의 깔끔한 보조 데이터 컬렉션을 기반으로 훈련된 오픈 소스 챗봇 생태계입니다.
GPT4All UI는 GPT4All 챗봇과 상호작용하기 위한 채팅 UI를 제공하는 Flask 웹 애플리케이션입니다.
Alpaca.cpp는 장치에서 로컬로 빠른 ChatGPT와 유사한 모델입니다. 이는 LLaMA 기반 모델과 Stanford Alpaca의 공개 재현, 지침(ChatGPT 교육에 사용되는 RLHF와 유사)을 따르도록 기본 모델을 미세 조정하고 채팅 인터페이스를 추가하기 위해 llama.cpp에 대한 일련의 수정 사항을 결합합니다.
llama.cpp는 C/C++로 된 Facebook의 LLaMA 모델 포트입니다.
OpenPlayground는 장치에서 로컬로 ChatGPT와 유사한 모델을 실행하기 위한 놀이터입니다.
Vicuna는 LLaMA를 미세 조정하여 훈련된 오픈 소스 챗봇입니다. chatgpt의 90% 이상의 품질을 달성했으며 훈련 비용은 300달러입니다.
Yeagar ai는 AI 기반 에이전트를 쉽게 구축, 프로토타입 및 배포할 수 있도록 설계된 Langchain Agent 제작자입니다.
Vicuna는 공개 API를 통해 ShareGPT.com에서 수집한 약 70,000개의 사용자 공유 대화를 사용하여 LLaMA 기본 모델을 미세 조정하여 만들어졌습니다. 데이터 품질을 보장하기 위해 HTML을 다시 마크다운으로 변환하고 부적절하거나 품질이 낮은 샘플을 필터링합니다.
ShareGPT는 한 번의 클릭으로 가장 격렬한 ChatGPT 대화를 공유할 수 있는 장소입니다. 지금까지 198,404개의 대화가 공유되었습니다.
FastChat은 대규모 언어 모델 기반 챗봇을 교육, 제공 및 평가하기 위한 개방형 플랫폼입니다.
Haystack은 Transformer 모델 및 LLM(GPT-4, ChatGPT 등)을 사용하여 데이터와 상호 작용하는 오픈 소스 NLP 프레임워크입니다. 복잡한 의사 결정, 질문 답변, 의미 체계 검색, 텍스트 생성 애플리케이션 등을 신속하게 구축할 수 있는 프로덕션 준비 도구를 제공합니다.
StableLM(Stability AI Language Models)은 StableLM 언어 모델 시리즈이며 새로운 체크포인트로 지속적으로 업데이트됩니다.
Databricks의 Dolly는 상업적 사용이 허가된 Databricks 기계 학습 플랫폼에서 훈련된 명령을 따르는 대규모 언어 모델입니다.
GPTCach는 LLM 쿼리용 의미 체계 캐시를 생성하기 위한 라이브러리입니다.
AlaC는 인공 지능 인프라형 코드 생성기입니다.
Adrenaline은 코드베이스와 대화할 수 있는 도구입니다. 정적 분석, 벡터 검색 및 대규모 언어 모델을 기반으로 합니다.
OpenAssistant는 작업을 이해하고, 타사 시스템과 상호 작용하고, 이를 위해 동적으로 정보를 검색할 수 있는 채팅 기반 도우미입니다.
DoctorGPT는 애플리케이션 로그에서 문제를 모니터링하고 진단하는 경량의 독립형 바이너리입니다.
HttpGPT는 비동기 REST 요청을 통해 OpenAI의 GPT 기반 서비스(ChatGPT 및 DALL-E)와의 통합을 촉진하는 Unreal Engine 5 플러그인으로, 개발자가 이러한 서비스와 쉽게 통신할 수 있도록 해줍니다. 또한 Chat GPT 및 DALL-E 이미지 생성을 엔진에 직접 통합하는 편집기 도구도 포함되어 있습니다.
PaLM 2는 기계 학습 및 책임 있는 AI에 대한 Google의 획기적인 연구 유산을 기반으로 하는 차세대 대규모 언어 모델입니다. 여기에는 코드 및 수학, 분류 및 질문 답변, 번역 및 다국어 숙련도, 이전 최첨단 LLM보다 뛰어난 자연어 생성을 포함한 고급 추론 작업이 포함됩니다.
Med-PaLM은 의료 질문에 대한 고품질 답변을 제공하도록 설계된 LLM(대형 언어 모델)입니다. 이는 Google이 세심하게 선별한 의료 전문가 시연 세트를 통해 의료 분야에 맞게 조정한 Google의 대규모 언어 모델의 강력한 기능을 활용합니다.
Sec-PaLM은 조직의 안전을 책임지는 사람들을 돕는 능력을 가속화하는 대규모 언어 모델(LLM)입니다. 이러한 새로운 모델은 사람들에게 보안을 이해하고 관리할 수 있는 보다 자연스럽고 창의적인 방법을 제공할 뿐만 아니라
맨 위로 돌아가기
맨 위로 돌아가기
상단으로 돌아갑니다
LocalAi는 자체 주최, 지역 사회 중심의 지역 개방형 API입니다. GPU가 필요없는 소비자 등급 하드웨어에서 Openai Running LLM에 대한 드롭 인 교체. LLAMA, GPT4ALL, RWKV, WHOSPER, VICUNA, KOALA, GPT4ALL-J, CEREBRAS, FALCON, DOLLY, StarCoder 등 GGML 호환 모델을 실행하는 것은 API입니다.
llama.cpp는 C/C ++의 Facebook의 LLAMA 모델 포트입니다.
Ollama는 Llama 2 및 기타 대형 언어 모델로 현지에서 일어나서 달리기위한 도구입니다.
LocalAi는 자체 주최, 지역 사회 중심의 지역 개방형 API입니다. GPU가 필요없는 소비자 등급 하드웨어에서 Openai Running LLM에 대한 드롭 인 교체. LLAMA, GPT4ALL, RWKV, WHOSPER, VICUNA, KOALA, GPT4ALL-J, CEREBRAS, FALCON, DOLLY, StarCoder 등 GGML 호환 모델을 실행하는 것은 API입니다.
Serge는 llama.cpp를 통해 Alpaca와 채팅하기위한 웹 인터페이스입니다. 사용하기 쉬운 API와 함께 완전히 자체 주최 및 도커 화.
OpenLlm은 프로덕션에서 LLM (Large Language Models)을 운영하기위한 개방형 플랫폼입니다. LLM을 쉽게 미세 조정, 서빙, 배포 및 모니터링하십시오.
Llama-Gpt는 자체 주최, 오프라인 Chatgpt와 같은 챗봇입니다. Llama 2. 100% Private로 구동되며 장치를 떠나는 데이터가 없습니다.
LLAMA2 WebUI는 GPU 또는 CPU (Linux/Windows/Mac)의 Gradio UI를 사용하여 LLAMA 2를 로컬로 실행하는 도구입니다. 생성 에이전트/앱의 llama2-wrapper 로컬 LLAMA2 백엔드로 사용하십시오.
llama2.c는 Pytorch에서 LLAMA 2 LLM 아키텍처를 훈련시킨 다음 하나의 간단한 700 라인 C 파일 (run.c)으로 추론하는 도구입니다.
Alpaca.cpp는 장치에서 로컬에서 빠른 Chatgpt와 같은 모델입니다. Llama Foundation 모델과 Stanford Alpaca의 개방형 재생산을 결합하여 기본 모델을 미세 조정하여 지침 (Chatgpt를 훈련시키는 데 사용되는 RLHF와 유사함)과 LLAMA.CPP에 대한 일련의 수정 세트를 채팅 인터페이스를 추가합니다.
GPT4ALL은 LLAMA를 기반으로 한 코드, 스토리 및 대화를 포함한 클린 보조 데이터의 대규모 컬렉션에 대해 교육을받은 오픈 소스 챗봇의 생태계입니다.
Minigpt-4
Lollms Webui는 LLM (대형 언어 모델) 모델의 허브입니다. 광범위한 작업을 위해 다양한 LLM 모델에 액세스하고 활용하기 위해 사용자 친화적 인 인터페이스를 제공하는 것을 목표로합니다. 글쓰기, 코딩, 데이터 구성, 이미지 생성 또는 질문에 대한 답변에 대한 도움이 필요한지 여부.
LM Studio는 로컬 LLM을 발견, 다운로드 및 실행하는 도구입니다.
Gradio Web UI는 대형 언어 모델을위한 도구입니다. 트랜스포머, GPTQ, LLAMA.CPP (GGML/GGUF), LLAMA 모델을 지원합니다.
OpenPlayGroun
Vicuna는 Fine Tuning Llama로 교육을받은 오픈 소스 챗봇입니다. 그것은 90% 이상의 Chatgpt 품질을 달성하고 훈련하는 데 $ 300의 비용이 들었습니다.
YeaGar AI는 AI 기반 에이전트를 쉽게 구축, 프로토 타입 및 배포하는 데 도움이되도록 설계된 Langchain 에이전트 제작자입니다.
KoboldCPP는 GGML 모델을위한 사용하기 쉬운 AI 텍스트 생성 소프트웨어입니다. Concedo에서 배포 할 수있는 단일 자체로 Llama.cpp를 구축하고 다양한 Kobold API 엔드 포인트, 추가 형식 지원, 뒤로 호환성, 지속적인 스토리, 도구 편집, 메모리, 메모리, 세계 저장 정보, 저자의 메모, 문자 및 시나리오.
상단으로 돌아갑니다
퍼지 로직은보다 고급 의사 결정 트리 처리와 규칙 기반 프로그래밍과의 통합을 더 잘 통합 할 수있는 휴리스틱 접근법입니다.
퍼지 논리 시스템의 아키텍처. 출처 : ResearchGate
SVM (Support Vector Machine)은 2 그룹 분류 문제에 분류 알고리즘을 사용하는 감독 된 기계 학습 모델입니다.
지지 벡터 머신 (SVM). 출처 : OpenClipart
신경망은 기계 학습의 하위 집합이며 딥 러닝 알고리즘의 핵심입니다. 이름/구조는 생물학적 뉴런/노드가 서로 신호를 보내는 과정을 복사하는 인간 뇌에서 영감을 얻습니다.
깊은 신경망. 출처 : IBM
Convolutional Neural Networks (R-CNN)는 이미지를 먼저 세그어링하여 잠재적 인 관련 경계 상자를 찾은 다음 감지 알고리즘을 실행하여 경계 박스에서 가장 가능성있는 객체를 찾는 객체 감지 알고리즘입니다.
컨볼 루션 신경 네트워크. 출처 : CS231N
재발 성 신경망 (RNN)은 순차적 데이터 또는 시계열 데이터를 사용하는 인공 신경망의 한 유형입니다.
재발 성 신경망. 출처 : Slideteam
다층 퍼셉트론 (MLP)은 임계 값 활성화를 갖는 다중 층의 퍼셉트론으로 구성된 다층 신경망이다.
다층 퍼셉트론. 출처 : Deepai
Random Forest는 일반적으로 사용되는 머신 러닝 알고리즘으로 여러 의사 결정 트리의 출력을 결합하여 단일 결과에 도달합니다. 숲의 의사 결정 트리는 샘플링과 예측 선택을 위해 가지 치기 할 수 없습니다. 분류 및 회귀 문제를 모두 처리함에 따라 사용의 용이성과 유연성은 채택에 연료를 공급했습니다.
임의의 숲. 출처 : Wikimedia
의사 결정 트리는 분류 및 회귀를위한 트리 구조 모델입니다.
** 의사 결정 트리. 출처 : CMU
Naive Bayes는 해결 된 Calssification 문제를 사용하는 머신 러닝 알고리즘입니다. 그것은 특징들 사이에 강력한 독립 가정을 가진 베이 에스 정리를 적용하는 것을 기반으로합니다.
베이 에스 정리. 출처 : Mathisfun
상단으로 돌아갑니다
Pytorch는 컴퓨터 비전 및 자연어 처리와 같은 응용 프로그램에 사용되는 연구에서 생산으로의 경로를 가속화하는 오픈 소스 딥 러닝 프레임 워크입니다. Pytorch는 Facebook의 AI Research Lab에서 개발했습니다.
Pytorch를 시작합니다
Pytorch 문서
Pytorch 토론 포럼
최고의 Pytorch 코스 온라인 | Coursera
최고의 Pytorch 코스 온라인 | 유데미
온라인 코스 및 수업으로 Pytorch를 배우십시오 | EDX
Pytorch 기초 - 학습 | 마이크로 소프트 문서
Pytorch로 딥 러닝에 소개 | udacity
Visual Studio Code의 Pytorch 개발
Azure의 Pytorch- Pytorch와의 딥 러닝 | Microsoft Azure
Pytorch -Azure Databricks | 마이크로 소프트 문서
Pytorch와의 딥 러닝 | 아마존 웹 서비스 (AWS)
Google Cloud에서 Pytorch를 시작합니다
Pytorch Mobile은 iOS 및 Android 모바일 장치 용 교육에서 배포까지 엔드 투 엔드 ML 워크 플로입니다.
Torchscript는 Pytorch 코드에서 직렬화 가능하고 최적화 가능한 모델을 작성하는 방법입니다. 이를 통해 Python 프로세스에서 모든 TorchScript 프로그램을 저장하고 Python 의존성이없는 프로세스에로드 할 수 있습니다.
Torchserve는 Pytorch 모델을 제공하는 유연하고 사용하기 쉬운 도구입니다.
Keras는 Python으로 작성되었으며 Tensorflow, CNTK 또는 Theano 위에서 실행할 수있는 고급 신경망 API로 빠른 실험을 가능하게하는 데 중점을두고 개발되었습니다. Tensorflow, Microsoft Cognitive Toolkit, R, Theano 또는 Plaidml 위에서 실행할 수 있습니다.
ONNX 런타임은 크로스 플랫폼, 고성능 ML 추론 및 훈련 가속기입니다. Pytorch 및 Tensorflow/Keras와 같은 딥 러닝 프레임 워크의 모델과 Scikit-Learn, LightGBM, Xgboost 등과 같은 클래식 머신 러닝 라이브러리의 모델을 지원합니다.
Kornia는 일반적인 CV (컴퓨터 비전) 문제를 해결하기위한 일련의 루틴 및 차별화 가능한 모듈로 구성된 차별화 가능한 컴퓨터 비전 라이브러리입니다.
Pytorch-NLP는 파이썬의 자연 언어 처리 (NLP)를위한 라이브러리입니다. 최신 연구를 염두에두고 구축되었으며 첫날부터 빠른 프로토 타이핑을 지원하도록 설계되었습니다. Pytorch-NLP에는 미리 훈련 된 임베딩, 샘플러, 데이터 세트 로더, 메트릭, 신경망 모듈 및 텍스트 인코더가 제공됩니다.
Ignite는 Pytorch의 신경 네트워크를 유연하고 투명하게 훈련하고 평가하는 데 도움이되는 고급 라이브러리입니다.
Hummingbird는 훈련 된 전통적인 ML 모델을 텐서 계산으로 컴파일하기위한 도서관입니다. 이를 통해 사용자는 신경망 네트워크 프레임 워크 (예 : Pytorch)를 완벽하게 활용하여 기존 ML 모델을 가속화 할 수 있습니다.
딥 그래프 라이브러리 (DGL)는 Pytorch 및 기타 프레임 워크 위에 그래프 신경망 모델 패밀리를 쉽게 구현할 수 있도록 구축 된 Python 패키지입니다.
Tensorly는 텐서 학습을 간단하게 만드는 것을 목표로하는 파이썬의 텐서 방법과 깊은 텐서화 된 신경망을위한 높은 수준의 API입니다.
Gpytorch는 확장 가능하고 유연한 가우스 프로세스 모델을 만들기 위해 설계된 Pytorch를 사용하여 구현 된 가우스 프로세스 라이브러리입니다.
Poutyne은 Pytorch의 케라 같은 프레임 워크이며 신경망을 훈련시키는 데 필요한 많은 보일러 플래팅 코드를 처리합니다.
Forte는 복합 가능한 구성 요소, 편리한 데이터 인터페이스 및 크로스 작업 상호 작용을 갖춘 NLP 파이프 라인을 구축하기위한 툴킷입니다.
Torchmetrics는 분산되고 확장 가능한 Pytorch 응용 프로그램을위한 머신 러닝 메트릭입니다.
Captum은 Pytorch에 구축 된 모델 해석 가능성을위한 오픈 소스, 확장 가능한 라이브러리입니다.
Transformer는 Pytorch, Tensorflow 및 Jax를위한 최첨단 자연 언어 처리입니다.
Hydra는 우아하게 복잡한 응용 프로그램을 구성하기위한 프레임 워크입니다.
Accelerate는 멀티 GPU, TPU, Mixed-Precision과 함께 Pytorch 모델을 훈련하고 사용하는 간단한 방법입니다.
Ray는 분산 응용 프로그램을 구축하고 실행하기위한 빠르고 간단한 프레임 워크입니다.
Parlai는 많은 작업에서 대화 모델을 공유, 교육 및 평가하기위한 통합 플랫폼입니다.
Pytorchvideo는 비디오 이해 연구를위한 딥 러닝 라이브러리입니다. 다양한 비디오 중심 모델, 데이터 세트, 교육 파이프 라인 등을 호스팅합니다.
Opacus는 차등 프라이버시로 Pytorch 모델을 훈련시킬 수있는 라이브러리입니다.
Pytorch Lightning은 Pytorch를위한 Keras와 같은 ML 라이브러리입니다. 그것은 당신에게 핵심 훈련과 검증 논리를 남기고 나머지를 자동화합니다.
Pytorch 기하학적 시간은 Pytorch 기하학을위한 시간적 (동적) 확장 라이브러리입니다.
Pytorch Geometric은 그래프, 포인트 클라우드 및 매니 폴드와 같은 불규칙한 입력 데이터에 대한 딥 러닝을위한 라이브러리입니다.
Raster Vision은 위성 및 항공 이미지에서 딥 러닝을위한 오픈 소스 프레임 워크입니다.
Crypten은 ML을 보존하는 개인 정보를위한 프레임 워크입니다. 그 목표는 ML 실무자가 안전한 컴퓨팅 기술을 이용할 수 있도록하는 것입니다.
Optuna는 하이퍼 파라미터 검색을 자동화하기위한 오픈 소스 하이퍼 파라미터 최적화 프레임 워크입니다.
Pyro는 Python으로 작성되고 백엔드에서 Pytorch가 지원하는 보편적 인 확률 론적 프로그래밍 언어 (PPL)입니다.
ALLUMentionations는 분류, 세분화, 객체 감지 및 포즈 추정과 같은 다양한 CV 작업을위한 빠르고 확장 가능한 이미지 증강 라이브러리입니다.
Skorch는 Pytorch를위한 고급 라이브러리로 전체 Scikit-Learn 호환성을 제공합니다.
MMF는 Facebook AI Research (Fair)의 비전 및 언어 다중 모드 연구를위한 모듈 식 프레임 워크입니다.
AdaptDL은 자원 적응 적 딥 러닝 교육 및 스케줄링 프레임 워크입니다.
Polyaxon은 대규모 딥 러닝 애플리케이션을 구축, 교육 및 모니터링하는 플랫폼입니다.
Textbrewer는 자연어 처리를위한 Pytorch 기반 지식 증류 툴킷입니다.
Advertorch는 적대적 견고성 연구를위한 도구 상자입니다. 여기에는 적대 예를 생성하고 공격에 대한 방어를위한 모듈이 포함되어 있습니다.
NEMO는 대화 AI를위한 AA 툴킷입니다.
ClinicAdl은 알츠하이머 병의 재현 가능한 분류를위한 틀입니다.
안정적인 기준 3 (SB3)은 Pytorch에서 강화 학습 알고리즘의 신뢰할 수있는 구현 세트입니다.
Torchio는 Pytorch로 작성된 딥 러닝 응용 프로그램에서 효율적으로 읽고, 전 프로세스, 샘플, 증강 및 쓸 수있는 일련의 도구입니다.
Pysyft는 암호화 된 개인 정보 보호 딥 러닝을위한 파이썬 라이브러리입니다.
Flair는 최첨단 자연 언어 처리 (NLP)를위한 매우 간단한 프레임 워크입니다.
Glow는 다양한 하드웨어 플랫폼에서 딥 러닝 프레임 워크의 성능을 가속화하는 ML 컴파일러입니다.
FairScale은 하나 또는 여러 기계/노드에 대한 고성능 및 대규모 교육을위한 Pytorch Extension 라이브러리입니다.
Monai는 의료 이미징 교육 워크 플로우를 개발하기위한 도메인 최적화 된 기본 기능을 제공하는 딥 러닝 프레임 워크입니다.
PFRL은 Pytorch를 사용하여 Python의 다양한 최첨단 심층 강화 알고리즘을 구현하는 심층 강화 학습 라이브러리입니다.
Einops는 읽을 수 있고 신뢰할 수있는 코드를위한 유연하고 강력한 텐서 작업입니다.
Pytorch3D는 Pytorch를 사용한 3D 컴퓨터 비전 연구를위한 효율적이고 재사용 가능한 구성 요소를 제공하는 딥 러닝 라이브러리입니다.
Ensemble Pytorch는 Pytorch가 딥 러닝 모델의 성능과 견고성을 향상시키기위한 통합 앙상블 프레임 워크입니다.
가볍게 자체 감독 학습을위한 컴퓨터 비전 프레임 워크입니다.
Higher는 자의적으로 복잡한 그라디언트 기반 메타 학습 알고리즘과 Vanilla Pytorch와 함께 중첩 된 최적화 루프의 구현을 용이하게하는 라이브러리입니다.
Horovod는 딥 러닝 프레임 워크를위한 분산 교육 라이브러리입니다. Horovod는 분산 된 DL을 빠르고 쉽게 사용하기를 목표로합니다.
Pennylane은 양자 ML, 자동 차별화 및 하이브리드 양자 클래식 계산의 최적화를위한 라이브러리입니다.
DetCerron2는 객체 감지 및 세분화를위한 Fair의 차세대 플랫폼입니다.
Fastai는 현대 모범 사례를 사용하여 빠르고 정확한 신경망을 단순화하는 도서관입니다.
상단으로 돌아갑니다
Tensorflow는 기계 학습을위한 엔드 투 엔드 오픈 소스 플랫폼입니다. 이 회사는 공동적이고 유연한 도구, 라이브러리 및 커뮤니티 리소스의 생태계를 보유하고있어 연구원들이 ML의 최첨단을 밀고 개발자가 ML 전원 응용 프로그램을 쉽게 구축하고 배포 할 수 있도록합니다.
Tensorflow로 시작합니다
Tensorflow 튜토리얼
Tensorflow 개발자 인증서 | 텐서플로우
텐서 플로 커뮤니티
텐서 플로우 모델 및 데이터 세트
텐서 플로우 클라우드
기계 학습 교육 | 텐서플로우
온라인 최고의 텐서 플로우 코스 | Coursera
온라인 최고의 텐서 플로우 코스 | 유데미
Tensorflow를 사용한 딥 러닝 | 유데미
Tensorflow를 사용한 딥 러닝 | EDX
딥 러닝을위한 텐서 플로우에 대한 소개 | udacity
텐서 플로우에 대한 소개 : 머신 러닝 충돌 코스 | Google 개발자
Tensorflow 모델 훈련 및 배포 -Azure Machine Learning
Python 및 Tensorflow를 사용하여 Azure 기능에 머신 러닝 모델을 적용 | Microsoft Azure
Tensorflow를 사용한 딥 러닝 | 아마존 웹 서비스 (AWS)
Tensorflow -Amazon Emr | AWS 문서
Tensorflow Enterprise | 구글 클라우드
Tensorflow Lite는 모바일 및 IoT 장치에 머신 러닝 모델을 배포하기위한 오픈 소스 딥 러닝 프레임 워크입니다.
TensorFlow.js는 JavaScript에서 ML 모델을 개발하거나 실행하고 ML을 브라우저 클라이언트 측면에서 직접, Node.js를 통해 서버 측에서 직접 ML을 사용할 수있는 JavaScript 라이브러리입니다. Raspberry Pi의 Node.js를 통한 장치.
Tensorflow_macos는 Apple의 ML Compute 프레임 워크를 사용하여 MACOS 11.0+ 가속 된 MACOS 11.0+ 용 MAC 최적화 버전의 TensorFlow 및 Tensorflow Addon입니다.
Google 공동 작업은 무료 Jupyter 노트북 환경으로 설정이 필요하지 않고 클라우드에서 완전히 실행되므로 한 번의 클릭으로 브라우저에서 텐서 플로우 코드를 실행할 수 있습니다.
What-IF 도구는 모델 이해, 디버깅 및 공정성에 유용한 머신 러닝 모델의 코드없는 프로브를위한 도구입니다. Tensorboard 및 Jupyter 또는 Colab 노트북으로 제공됩니다.
Tensorboard는 Tensorflow 프로그램을 이해, 디버그 및 최적화 할 수있는 시각화 도구 제품군입니다.
Keras는 Python으로 작성되었으며 Tensorflow, CNTK 또는 Theano 위에서 실행할 수있는 고급 신경망 API로 빠른 실험을 가능하게하는 데 중점을두고 개발되었습니다. Tensorflow, Microsoft Cognitive Toolkit, R, Theano 또는 Plaidml 위에서 실행할 수 있습니다.
XLA (가속 선형 대수)는 텐서 플로 계산을 최적화하는 선형 대수에 대한 도메인 별 컴파일러입니다. 결과는 서버 및 모바일 플랫폼의 속도, 메모리 사용 및 이식성이 향상됩니다.
ML Perf는 ML 소프트웨어 프레임 워크, ML 하드웨어 가속기 및 ML 클라우드 플랫폼의 성능을 측정하기위한 광범위한 ML 벤치 마크 제품군입니다.
Tensorflow Playground는 브라우저의 신경망을 사용하여 땜질하는 개발 환경입니다.
TPU Research Cloud (TRC)는 연구원이 다음 연구 혁신의 물결을 가속화 할 수 있도록 1,000 개 이상의 클라우드 TPU 클러스터에 대한 액세스를 신청할 수있는 프로그램입니다.
MLIR은 새로운 중간 표현 및 컴파일러 프레임 워크입니다.
격자는 상식적인 형상 제약 조건을 갖춘 유연하고 제어되고 해석 가능한 ML 솔루션을위한 라이브러리입니다.
Tensorflow Hub는 재사용 가능한 기계 학습을위한 라이브러리입니다. 최소한의 코드로 최신 훈련 된 모델을 다운로드하여 재사용하십시오.
Tensorflow Cloud는 로컬 환경을 Google 클라우드에 연결하는 라이브러리입니다.
TensorFlow 모델 최적화 툴킷은 배포 및 실행을위한 ML 모델을 최적화하기위한 도구 제품군입니다.
Tensorflow 추천자는 추천 시스템 모델을 구축하기위한 라이브러리입니다.
Tensorflow 텍스트는 텍스트 및 NLP 관련 클래스 및 OPS 모음으로 Tensorflow 2와 함께 사용할 수 있습니다.
Tensorflow 그래픽은 카메라, 조명 및 재료부터 렌더러에 이르기까지 컴퓨터 그래픽 기능 라이브러리입니다.
Tensorflow Federated는 기계 학습 및 분산 데이터에 대한 기타 계산을위한 오픈 소스 프레임 워크입니다.
Tensorflow 확률은 확률 적 추론 및 통계 분석을위한 라이브러리입니다.
Tensor2tensor는 딥 러닝을보다 접근하기 쉽고 ML 연구를 가속화하도록 설계된 딥 러닝 모델 및 데이터 세트 라이브러리입니다.
Tensorflow Privacy는 차별화 된 개인 정보를 갖춘 기계 학습 모델을위한 Tensorflow Optimizers의 구현을 포함하는 Python 라이브러리입니다.
Tensorflow Ranking은 Tensorflow 플랫폼의 LTR (Learning-to Rank) 기술을위한 라이브러리입니다.
텐서 플로우 에이전트는 텐서 플로우에서 강화 학습을위한 라이브러리입니다.
Tensorflow Addons는 잘 확립 된 API 패턴을 준수하는 기여의 저장소이지만 SIG Addons가 유지 관리하는 코어 텐서 플로우에서는 사용할 수없는 새로운 기능을 구현합니다. Tensorflow는 기본적으로 많은 연산자, 레이어, 메트릭, 손실 및 최적화를 지원합니다.
Tensorflow I/O는 SIG IO가 관리하는 데이터 세트, 스트리밍 및 파일 시스템 확장입니다.
Tensorflow Quantum은 하이브리드 양자 클래식 ML 모델의 빠른 프로토 타이핑을위한 양자 머신 러닝 라이브러리입니다.
도파민은 강화 학습 알고리즘의 빠른 프로토 타이핑을위한 연구 프레임 워크입니다.
TRFL은 DeepMind가 만든 강화 학습 빌딩 블록을위한 도서관입니다.
Mesh Tensorflow는 분산 딥 러닝을위한 언어로, 광범위한 분산 텐서 계산을 지정할 수 있습니다.
RaggedTensors는 텍스트 (단어, 문장, 문자) 및 가변 길이의 배치를 포함하여 불균일 한 모양으로 데이터를 쉽게 저장하고 조작 할 수있는 API입니다.
Unicode Ops는 Tensorflow에서 유니 코드 텍스트 작업을 지원하는 API입니다.
Magenta는 예술과 음악을 만드는 과정에서 기계 학습의 역할을 탐구하는 연구 프로젝트입니다.
Nucleus는 Sam 및 VCF와 같은 공통 유전체학 파일 형식의 데이터를 쉽게 읽고 쓰고 쓰고 분석 할 수 있도록 설계된 Python 및 C ++ 코드 라이브러리입니다.
Sonnet은 신경망을 구성하기위한 DeepMind의 라이브러리입니다.
신경 구조 학습은 피처 입력 외에도 구조화 된 신호를 활용하여 신경망을 훈련시키는 학습 프레임 워크입니다.
Model Remediation은 기본 성능 바이어스로 인한 사용자 피해를 줄이거 나 제거하는 방식으로 모델을 만들고 훈련시키는 데 도움이되는 라이브러리입니다.
공정성 지표는 이진 및 멀티 클래스 분류기에 대한 일반적으로 식별 된 공정성 지표를 쉽게 계산할 수있는 라이브러리입니다.
Decision Forests는 분류, 회귀 및 순위를 위해 의사 결정 숲을 사용하는 모델 교육, 서비스 및 해석을위한 최첨단 알고리즘입니다.
상단으로 돌아갑니다
Core ML은 기계 학습 모델을 Apple 장치 (iOS, WatchOS, MacOS 및 TVOS 포함)에서 실행중인 앱에 통합하기위한 Apple 프레임 워크입니다. Core ML은 심층 신경망 (Convolutional 및 Reburrent), 부스팅이 장착 된 트리 앙상블 및 일반화 된 선형 모델을 포함한 광범위한 ML 방법 세트에 대한 공개 파일 형식 (.mlmodel)을 소개합니다. 이 형식의 모델은 Xcode를 통해 앱에 직접 통합 될 수 있습니다.
핵심 ML 소개
핵심 ML 모델을 앱에 통합합니다
핵심 ML 모델
핵심 ML API 참조
핵심 ML 사양
Core ML의 Apple 개발자 포럼
최고 핵심 ML 코스 온라인 | 유데미
최고 핵심 ML 코스 온라인 | Coursera
Core ML |를위한 IBM WATSON 서비스 IBM
IBM Maximo Visual 검사를 사용하여 핵심 ML 자산 생성 | IBM
Core ML 도구는 핵심 ML 모델 변환, 편집 및 검증을위한 지원 도구가 포함 된 프로젝트입니다.
ML 작성 MAC에서 기계 학습 모델을 교육하는 새로운 방법을 제공하는 도구입니다. 강력한 핵심 ML 모델을 생성하면서 모델 교육에서 복잡성을 취합니다.
Tensorflow_macos는 Apple의 ML Compute 프레임 워크를 사용하여 MACOS 11.0+ 가속 된 MACOS 11.0+ 용 MAC 최적화 버전의 TensorFlow 및 Tensorflow Addon입니다.
Apple Vision은 얼굴 및 얼굴 랜드 마크 감지, 텍스트 감지, 바코드 인식, 이미지 등록 및 일반 기능 추적을 수행하는 프레임 워크입니다. 비전은 또한 분류 또는 객체 감지와 같은 작업에 맞춤형 코어 ML 모델을 사용할 수 있습니다.
Keras는 Python으로 작성되었으며 Tensorflow, CNTK 또는 Theano 위에서 실행할 수있는 고급 신경망 API로 빠른 실험을 가능하게하는 데 중점을두고 개발되었습니다. Tensorflow, Microsoft Cognitive Toolkit, R, Theano 또는 Plaidml 위에서 실행할 수 있습니다.
Xgboost는 매우 효율적이고 유연하며 휴대용으로 설계된 최적화 된 분산 그라디언트 부스트 라이브러리입니다. 그라디언트 부스트 프레임 워크에서 기계 학습 알고리즘을 구현합니다. Xgboost는 빠르고 정확한 방식으로 많은 데이터 과학 문제를 해결하는 평행 트리 부스트 (GBDT, GBM이라고도 함)를 제공합니다. AWS, GCE, AZURE 및 원사 클러스터를 포함한 여러 기계에 대한 분산 교육을 지원합니다. 또한 Flink, Spark 및 기타 클라우드 데이터 흐름 시스템과 통합 될 수 있습니다.
LIBSVM은 지원 벡터 분류, (C-SVC, NU-SVC), 회귀 (Epsilon-SVR, NU-SVR) 및 배포 추정 (1 등급 SVM)을위한 통합 소프트웨어입니다. 멀티 클래스 분류를 지원합니다.
Scikit-Learn은 데이터 마이닝 및 데이터 분석을위한 간단하고 효율적인 도구입니다. Numpy, Scipy 및 Mathplotlib에 기반을두고 있습니다.
Xcode에는 개발자가 Mac, iPhone, iPad, Apple TV 및 Apple Watch에 대한 훌륭한 응용 프로그램을 만들기 위해 필요한 모든 것이 포함되어 있습니다. Xcode는 개발자에게 사용자 인터페이스 설계, 코딩, 테스트 및 디버깅을위한 통합 워크 플로를 제공합니다. Xcode는 인텔 기반 CPU 및 Apple Silicon에서 기본적으로 100% 실행되는 범용 앱으로 제작되었습니다. 여기에는 Apple Silicon 및 Intel x86_64 CPU에서 기본적으로 실행되는 앱을 구축하는 데 필요한 모든 프레임 워크, 컴파일러, 디버거 및 기타 도구가 포함 된 통합 MACOS SDK가 포함되어 있습니다.
Swiftui는 앱의 사용자 인터페이스를 선언하기위한 뷰, 컨트롤 및 레이아웃 구조를 제공하는 사용자 인터페이스 툴킷입니다. Swiftui 프레임 워크는 탭, 제스처 및 기타 유형의 입력을 애플리케이션에 제공하기위한 이벤트 처리기를 제공합니다.
Uikit은 iOS 또는 TVOS 앱에 필요한 인프라를 제공하는 프레임 워크입니다. 인터페이스를 구현하기위한 Wind
AppKit은 Windows, 패널, 버튼, 메뉴, 스크롤러 및 텍스트 필드와 같은 MACOS 앱의 사용자 인터페이스를 구현하는 데 필요한 모든 객체를 포함하는 그래픽 사용자 인터페이스 툴킷이며 모든 세부 사항을 효율적으로 처리합니다. 화면에 그리기, 하드웨어 장치 및 화면 버퍼와 통신하고 그리기 전에 화면의 영역을 지우고 클립 뷰를 지 웁니다.
Arkit은 개발자가 Apple에서 개발 한 iOS 용 Augmented Reality 앱을 구축 할 수있는 소프트웨어 개발 도구 세트 세트입니다. 최신 버전 Arkit 3.5는 iPad Pro (2020)의 새로운 LIDAR 스캐너 및 깊이 감지 시스템을 활용하여 장면 지오메트리를 사용하여 장면 이해 및 객체 폐색을 사용하는 새로운 세대의 AR 앱을 지원합니다.
RealityKit은 가상 객체를 현실 세계에 원활하게 통합하기 위해 Arkit Framework가 제공 한 정보로 고성능 3D 시뮬레이션을 구현하고 렌더링하는 프레임 워크입니다.
SceneKit은 고급 3D 그래픽 프레임 워크로 iOS 앱에서 3D 애니메이션 장면 및 효과를 만들 수 있습니다.
Instruments는 Xcode 도구 세트의 일부인 강력하고 유연한 성능 분석 및 테스트 도구입니다. 동작 및 성능을 더 잘 이해하고 최적화하기 위해 iOS, WatchOS, TVOS 및 MacOS 앱, 프로세스 및 장치를 프로필로 제공하도록 설계되었습니다.
Cocoapods는 간단한 텍스트 파일로 프로젝트의 종속성을 지정하여 Xcode 프로젝트에 사용되는 Swift 및 Objective-C의 종속성 관리자입니다. 그런 다음 Cocoapods는 라이브러리 간의 종속성을 재귀 적으로 해결하고 모든 종속성에 대한 소스 코드를 가져 오며 프로젝트를 구축하기 위해 Xcode Workspace를 생성하고 유지합니다.
앱 코드는 코드의 품질을 지속적으로 모니터링하고 있습니다. 그것은 당신에게 오류와 냄새에 대해 경고하며 자동으로 해결하기 위해 빠른 고정을 제안합니다. AppCode는 Objective-C, Swift, C/C ++ 및 기타 지원되는 언어에 대한 많은 코드 검사에 대한 많은 코드 검사를 제공합니다.
상단으로 돌아갑니다
딥 러닝은 기계 학습의 하위 집합으로, 본질적으로 3 개 이상의 계층을 가진 신경망입니다. 그러나 이러한 신경망은 인간 뇌의 행동을 시뮬레이션하려고 시도하지만, 그 능력과 일치합니다. 이를 통해 신경망은 대량의 데이터에서 "학습"할 수 있습니다. 학습은 감독, 반 감독 또는 감독 할 수 있습니다.
딥 러닝 온라인 코스 | 엔비디아
최고의 딥 러닝 과정 온라인 | Coursera
최고의 딥 러닝 과정 온라인 | 유데미
온라인 코스 및 레슨으로 딥 러닝을 배우십시오 | EDX
딥 러닝 온라인 코스 Nanodegree | udacity
Andrew Ng의 기계 학습 과정 | Coursera
Andrew NG의 기계 학습 엔지니어링 (MLOPS) 코스 | Coursera
데이터 과학 : 파이썬의 딥 러닝 및 신경망 | 유데미
파이썬으로 머신 러닝 이해 | Pluralsight
머신 러닝 알고리즘에 대해 생각하는 방법 | Pluralsight
딥 러닝 코스 | 스탠포드 온라인
딥 러닝 - UW 전문 및 평생 교육
딥 러닝 온라인 코스 | 하버드 대학교
모든 사람을위한 기계 학습 과정 | 데이터캠프
인공 지능 전문가 과정 : 플래티넘 에디션 | 유데미
최고의 인공 지능 과정 온라인 | Coursera
온라인 코스 및 수업으로 인공 지능을 배우십시오 | EDX
인공 지능을위한 컴퓨터 과학의 전문 인증서 | EDX
인공 지능 Nanodegree 프로그램
인공 지능 (AI) 온라인 과정 | udacity
인공 지능 코스 소개 | udacity
IoT 개발자 코스 | udacity
추론 : 골 트리 및 규칙 기반 전문가 시스템 | MIT OpenCourseware
전문가 시스템 및 응용 인공 지능
자율 시스템 - Microsoft AI
Microsoft Project Bonsai 소개
Microsoft Autonomous Systems 플랫폼으로 기계 교육
자율 해상 시스템 훈련 | AMC 검색
온라인 최고의 자율 자동차 코스 | 유데미
응용 제어 시스템 1 : 자율 주차 : Math + PID + MPC | 유데미
온라인 과정과 수업으로 자율 로봇 공학을 배우십시오 | EDX
인공 지능 Nanodegree 프로그램
자율 시스템 온라인 과정 및 프로그램 | udacity
IoT 개발자 코스 | udacity
자율 시스템 MOOC 및 무료 온라인 과정 | MOOC 목록
로봇 공학 및 자율 시스템 대학원 프로그램 | 스탠드 포드 온라인
모바일 자율 시스템 실험실 | MIT OpenCourseware
NVIDIA CUDNN은 심층 신경망을위한 GPU로 된 프리미티브 라이브러리입니다. CUDNN은 전방 및 후진 컨볼 루션, 풀링, 정규화 및 활성화 층과 같은 표준 루틴에 대한 고도로 조정 된 구현을 제공합니다. CUDNN은 Caffe2, Chainer, Keras, Matlab, MXNet, Pytorch 및 Tensorflow를 포함하여 널리 사용되는 딥 러닝 프레임 워크를 가속화합니다.
NVIDIA DLSS (딥 러닝 슈퍼 샘플링)는 Geforce RTX ™ GPU의 전용 텐서 코어 AI 프로세서를 사용하여 그래픽 성능을 향상시키는 시간적 이미지 업 스케일링 AI 렌더링 기술입니다. DLSS는 딥 러닝 신경망의 힘을 사용하여 프레임 속도를 높이고 게임에 아름답고 날카로운 이미지를 생성합니다.
AMD FidelityFX Super Resolution (FSR)은 하위 해상도 입력에서 고해상도 프레임을 생성하기위한 오픈 소스의 고품질 솔루션입니다. 고품질 가장자리를 만드는 데 특히 중점을 둔 최첨단 딥 러닝 알고리즘 모음을 사용하여 기본 해상도의 직접 렌더링에 비해 성능이 크게 향상됩니다. FSR을 사용하면 AMD RDNA ™ 및 AMD RDNA ™ 2 아키텍처 용 하드웨어 레이 추적과 같은 비용이 많이 드는 렌더링 작업에 대한 "실제 성능"을 활성화합니다.
Intel XE Super Sampling (XESS)은 NVIDIA의 DLSS (딥 러닝 슈퍼 샘플링)와 유사한 그래픽 성능을 향상시키는 시간적 이미지 업 스케일링 AI 렌더링 기술입니다. 인텔의 아크 GPU 아키텍처 (2022 년 초)에는 XESS를 실행하기위한 전용 XE 코어를 특징으로하는 GPU가 있습니다. GPU에는 하드웨어로 인한 AI 프로세싱을위한 XE 매트릭스 확장 매트릭스 (XMX) 엔진이 있습니다. XESS는 통합 그래픽을 포함하여 XMX가없는 장치에서 실행할 수 있지만 XESS의 성능은 DP4A 명령으로 구동되기 때문에 intel 그래픽 카드에서 낮아집니다.
Jupyter Notebook은 라이브 코드, 방정식, 시각화 및 내러티브 텍스트를 포함하는 문서를 작성하고 공유 할 수있는 오픈 소스 웹 응용 프로그램입니다. Jupyter는 데이터 청소 및 변환, 수치 시뮬레이션, 통계 모델링, 데이터 시각화, 데이터 과학 및 기계 학습을 수행하는 산업에서 널리 사용됩니다.
Apache Spark는 대규모 데이터 처리를위한 통합 분석 엔진입니다. 스칼라, 자바, 파이썬 및 R에서 고급 API와 데이터 분석을위한 일반적인 계산 그래프를 지원하는 최적화 된 엔진을 제공합니다. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. 설치하다. 원칙. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
AutoGluon is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Weka is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Microsoft Project Bonsai is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
ROS/ROS2 bridge for CARLA(package) is a bridge that enables two-way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Robotics Toolbox™ is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Predictive Maintenance Toolbox™ is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird's-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Navigation Toolbox™ is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
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Reinforcement Learning is a subset of machine learning, which is a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain,though, far from matching its ability. This allows the neural networks to "learn" from a process in which a model learns to become more accurate for performing an action in an environment based on feedback in order to maximize the reward. The Learning can be supervised, semi-supervised or unsupervised.
Top Reinforcement Learning Courses | Coursera
Top Reinforcement Learning Courses | 유데미
Top Reinforcement Learning Courses | Udacity
Reinforcement Learning Courses | Stanford Online
Deep Learning Online Courses | 엔비디아
Top Deep Learning Courses Online | Coursera
Top Deep Learning Courses Online | 유데미
Learn Deep Learning with Online Courses and Lessons | edX
Deep Learning Online Course Nanodegree | Udacity
Machine Learning Course by Andrew Ng | Coursera
Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
Data Science: Deep Learning and Neural Networks in Python | 유데미
Understanding Machine Learning with Python | Pluralsight
How to Think About Machine Learning Algorithms | Pluralsight
Deep Learning Courses | Stanford Online
Deep Learning - UW Professional & Continuing Education
Deep Learning Online Courses | 하버드 대학교
Machine Learning for Everyone Courses | 데이터캠프
Artificial Intelligence Expert Course: Platinum Edition | 유데미
Top Artificial Intelligence Courses Online | Coursera
Learn Artificial Intelligence with Online Courses and Lessons | edX
Professional Certificate in Computer Science for Artificial Intelligence | edX
Artificial Intelligence Nanodegree program
Artificial Intelligence (AI) Online Courses | Udacity
Intro to Artificial Intelligence Course | Udacity
Edge AI for IoT Developers Course | Udacity
Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare
Expert Systems and Applied Artificial Intelligence
Autonomous Systems - Microsoft AI
Introduction to Microsoft Project Bonsai
Machine teaching with the Microsoft Autonomous Systems platform
Autonomous Maritime Systems Training | AMC Search
Top Autonomous Cars Courses Online | 유데미
Applied Control Systems 1: autonomous cars: Math + PID + MPC | 유데미
Learn Autonomous Robotics with Online Courses and Lessons | edX
Artificial Intelligence Nanodegree program
Autonomous Systems Online Courses & Programs | Udacity
Edge AI for IoT Developers Course | Udacity
Autonomous Systems MOOC and Free Online Courses | MOOC List
Robotics and Autonomous Systems Graduate Program | Standford Online
Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
OpenAI is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API.
ReinforcementLearning.jl is a collection of tools for doing reinforcement learning research in Julia.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.
AWS RoboMaker is a service that provides a fully-managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. 설치하다. 원칙. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
AutoGluon is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Weka is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Microsoft Project Bonsai is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
ROS/ROS2 bridge for CARLA(package) is a bridge that enables two-way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Robotics Toolbox™ is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Predictive Maintenance Toolbox™ is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird's-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
Navigation Toolbox™ is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
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Computer Vision is a field of Artificial Intelligence (AI) that focuses on enabling computers to identify and understand objects and people in images and videos.
OpenCV Courses
Exploring Computer Vision in Microsoft Azure
Top Computer Vision Courses Online | Coursera
Top Computer Vision Courses Online | 유데미
Learn Computer Vision with Online Courses and Lessons | edX
Computer Vision and Image Processing Fundamentals | edX
Introduction to Computer Vision Courses | Udacity
Computer Vision Nanodegree program | Udacity
Machine Vision Course |MIT Open Courseware
Computer Vision Training Courses | NobleProg
Visual Computing Graduate Program | Stanford Online
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Microsoft Computer Vision Recipes is a project that provides examples and best practice guidelines for building computer vision systems. This allows people to build a comprehensive set of tools and examples that leverage recent advances in Computer Vision algorithms, neural architectures, and operationalizing such systems. Creatin from existing state-of-the-art libraries and build additional utility around loading image data, optimizing and evaluating models, and scaling up to the cloud.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird's-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
LRSLibrary is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Statistics and Machine Learning Toolbox™ is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
Partial Differential Equation Toolbox™ is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Data Acquisition Toolbox™ is a tool that provides apps and functions for configuring data acquisition hardware, reading data into MATLAB® and Simulink®, and writing data to DAQ analog and digital output channels. The toolbox supports a variety of DAQ hardware, including USB, PCI, PCI Express®, PXI®, and PXI Express® devices, from National Instruments® and other vendors.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
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Natural Language Processing (NLP) is a branch of artificial intelligence (AI) focused on giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics rule-based modeling of human language with statistical, machine learning, and deep learning models.
Natural Language Processing With Python's NLTK Package
Cognitive Services—APIs for AI Developers | Microsoft Azure
Artificial Intelligence Services - Amazon Web Services (AWS)
Google Cloud Natural Language API
Top Natural Language Processing Courses Online | 유데미
Introduction to Natural Language Processing (NLP) | 유데미
Top Natural Language Processing Courses | Coursera
Natural Language Processing | Coursera
Natural Language Processing in TensorFlow | Coursera
Learn Natural Language Processing with Online Courses and Lessons | edX
Build a Natural Language Processing Solution with Microsoft Azure | Pluralsight
Natural Language Processing (NLP) Training Courses | NobleProg
Natural Language Processing with Deep Learning Course | Standford Online
Advanced Natural Language Processing - MIT OpenCourseWare
Certified Natural Language Processing Expert Certification | IABAC
Natural Language Processing Course - Intel
Natural Language Toolkit (NLTK) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.
spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It also features neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT.
CoreNLP is a set of natural language analysis tools written in Java. CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.
NLPnet is a Python library for Natural Language Processing tasks based on neural networks. It performs part-of-speech tagging, semantic role labeling and dependency parsing.
Flair is a simple framework for state-of-the-art Natural Language Processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.
Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Apache OpenNLP is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like Named Entity Recognition, Sentence Detection, POS(Part-Of-Speech) tagging, Tokenization Feature extraction, Chunking, Parsing, and Coreference resolution.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
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Bioinformatics is a field of computational science that has to do with the analysis of sequences of biological molecules. This usually refers to genes, DNA, RNA, or protein, and is particularly useful in comparing genes and other sequences in proteins and other sequences within an organism or between organisms, looking at evolutionary relationships between organisms, and using the patterns that exist across DNA and protein sequences to figure out what their function is.
European Bioinformatics Institute
National Center for Biotechnology Information
Online Courses in Bioinformatics |ISCB - International Society for Computational Biology
Bioinformatics | Coursera
Top Bioinformatics Courses | 유데미
Biometrics Courses | 유데미
Learn Bioinformatics with Online Courses and Lessons | edX
Bioinformatics Graduate Certificate | Harvard Extension School
Bioinformatics and Biostatistics | UC San Diego Extension
Bioinformatics and Proteomics - Free Online Course Materials | MIT
Introduction to Biometrics course - Biometrics Institute
Bioconductor is an open source project that provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, and an active user community. Bioconductor is also available as an AMI (Amazon Machine Image) and Docker images.
Bioconda is a channel for the conda package manager specializing in bioinformatics software. It has a repository of packages containing over 7000 bioinformatics packages ready to use with conda install.
UniProt is a freely accessible database that provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information.
Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (mammalian) genomes.
Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics.
BioRuby is a toolkit that has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO.
BioJava is a toolkit that provides an API to maintain local installations of the PDB, load and manipulate structures, perform standard analysis such as sequence and structure alignments and visualize them in 3D.
BioPHP is an open source project that provides a collection of open source PHP code, with classes for DNA and protein sequence analysis, alignment, database parsing, and other bioinformatics tools.
Avogadro is an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.
Ascalaph Designer is a program for molecular dynamic simulations. Under a single graphical environment are represented as their own implementation of molecular dynamics as well as the methods of classical and quantum mechanics of popular programs.
Anduril is a workflow platform for analyzing large data sets. Anduril provides facilities for analyzing high-thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.
Galaxy is an open source, web-based platform for accessible, reproducible, and transparent computational biomedical research. It allows users without programming experience to easily specify parameters and run individual tools as well as larger workflows. It also captures run information so that any user can repeat and understand a complete computational analysis.
PathVisio is a free open-source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins.
Orange is a powerful data mining and machine learning toolkit that performs data analysis and visualization.
Basic Local Alignment Search Tool is a tool that finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.
OSIRIS is public-domain, free, and open source STR analysis software designed for clinical, forensic, and research use, and has been validated for use as an expert system for single-source samples.
NCBI BioSystems is a Database that provides integrated access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez.
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CUDA Toolkit. Source: NVIDIA Developer CUDA
CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
CUDA Toolkit Documentation
CUDA Quick Start Guide
CUDA on WSL
CUDA GPU support for TensorFlow
NVIDIA Deep Learning cuDNN Documentation
NVIDIA GPU Cloud Documentation
NVIDIA NGC is a hub for GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
NVIDIA NGC Containers is a registry that provides researchers, data scientists, and developers with simple access to a comprehensive catalog of GPU-accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
CUDA Toolkit is a collection of tools & libraries that provide a development environment for creating high performance GPU-accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
CUDA-X HPC is a collection of libraries, tools, compilers and APIs that help developers solve the world's most challenging problems. CUDA-X HPC includes highly tuned kernels essential for high-performance computing (HPC).
NVIDIA Container Toolkit is a collection of tools & libraries that allows users to build and run GPU accelerated Docker containers. The toolkit includes a container runtime library and utilities to automatically configure containers to leverage NVIDIA GPUs.
Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
CUB is a cooperative primitives for CUDA C++ kernel authors.
Tensorman is a utility for easy management of Tensorflow containers by developed by System76.Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.
Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference.
CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
ArrayFire is a general-purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
Thrust is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
AresDB is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
Arraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
Kintinuous is a real-time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
GraphVite is a general graph embedding engine, dedicated to high-speed and large-scale embedding learning in various applications.
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MATLAB is a programming language that does numerical computing such as expressing matrix and array mathematics directly.
MATLAB Documentation
Getting Started with MATLAB
MATLAB and Simulink Training from MATLAB Academy
MathWorks Certification Program
MATLAB Online Courses from Udemy
MATLAB Online Courses from Coursera
MATLAB Online Courses from edX
Building a MATLAB GUI
MATLAB Style Guidelines 2.0
Setting Up Git Source Control with MATLAB & Simulink
Pull, Push and Fetch Files with Git with MATLAB & Simulink
Create New Repository with MATLAB & Simulink
PRMLT is Matlab code for machine learning algorithms in the PRML book.
MATLAB and Simulink Services & Applications List
MATLAB in the Cloud is a service that allows you to run in cloud environments from MathWorks Cloud to Public Clouds including AWS and Azure.
MATLAB Online™ is a service that allows to users to uilitize MATLAB and Simulink through a web browser such as Google Chrome.
Simulink is a block diagram environment for Model-Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
Simulink Online™ is a service that provides access to Simulink through your web browser.
MATLAB Drive™ is a service that gives you the ability to store, access, and work with your files from anywhere.
MATLAB Parallel Server™ is a tool that lets you scale MATLAB® programs and Simulink® simulations to clusters and clouds. You can prototype your programs and simulations on the desktop and then run them on clusters and clouds without recoding. MATLAB Parallel Server supports batch jobs, interactive parallel computations, and distributed computations with large matrices.
MATLAB Schemer is a MATLAB package makes it easy to change the color scheme (theme) of the MATLAB display and GUI.
LRSLibrary is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Statistics and Machine Learning Toolbox™ is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
Partial Differential Equation Toolbox™ is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
SoC Blockset™ is a tool that provides Simulink® blocks and visualization tools for modeling, simulating, and analyzing hardware and software architectures for ASICs, FPGAs, and systems on a chip (SoC). You can build your system architecture using memory models, bus models, and I/O models, and simulate the architecture together with the algorithms.
Wireless HDL Toolbox™ is a tool that provides pre-verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
ThingSpeak™ is an IoT analytics service that allows you to aggregate, visualize, and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak, you can perform online analysis and process data as it comes in. ThingSpeak is often used for prototyping and proof-of-concept IoT systems that require analytics.
SEA-MAT is a collaborative effort to organize and distribute Matlab tools for the Oceanographic Community.
Gramm is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
hctsa is a software package for running highly comparative time-series analysis using Matlab.
Plotly is a Graphing Library for MATLAB.
YALMIP is a MATLAB toolbox for optimization modeling.
GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
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C++ is a cross-platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
C is a general-purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.
Embedded C is a set of language extensions for the C programming language by the C Standards Committee to address issues that exist between C extensions for different embedded systems. The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.
C & C++ Developer Tools from JetBrains
Open source C++ libraries on cppreference.com
C++ Graphics libraries
C++ Libraries in MATLAB
C++ Tools and Libraries Articles
Google C++ Style Guide
Introduction C++ Education course on Google Developers
C++ style guide for Fuchsia
C and C++ Coding Style Guide by OpenTitan
Chromium C++ Style Guide
C++ Core Guidelines
C++ Style Guide for ROS
Learn C++
Learn C : An Interactive C Tutorial
C++ Institute
C++ Online Training Courses on LinkedIn Learning
C++ Tutorials on W3Schools
Learn C Programming Online Courses on edX
Learn C++ with Online Courses on edX
Learn C++ on Codecademy
Coding for Everyone: C and C++ course on Coursera
C++ For C Programmers on Coursera
Top C Courses on Coursera
C++ Online Courses on Udemy
Top C Courses on Udemy
Basics of Embedded C Programming for Beginners on Udemy
C++ For Programmers Course on Udacity
C++ Fundamentals Course on Pluralsight
Introduction to C++ on MIT Free Online Course Materials
Introduction to C++ for Programmers | 하버드
Online C Courses | 하버드 대학교
AWS SDK for C++
Azure SDK for C++
Azure SDK for C
C++ Client Libraries for Google Cloud Services
Visual Studio is an integrated development environment (IDE) from Microsoft; which is a feature-rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
Visual Studio Code is a code editor redefined and optimized for building and debugging modern web and cloud applications.
Vcpkg is a C++ Library Manager for Windows, Linux, and MacOS.
ReSharper C++ is a Visual Studio Extension for C++ developers developed by JetBrains.
AppCode is constantly monitoring the quality of your code. It warns you of errors and smells and suggests quick-fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.
CLion is a cross-platform IDE for C and C++ developers developed by JetBrains.
Code::Blocks is a free C/C++ and Fortran IDE built to meet the most demanding needs of its users. It is designed to be very extensible and fully configurable. Built around a plugin framework, Code::Blocks can be extended with plugins.
CppSharp is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem. It consumes C/C++ header and library files and generates the necessary glue code to surface the native API as a managed API. Such an API can be used to consume an existing native library in your managed code or add managed scripting support to a native codebase.
Conan is an Open Source Package Manager for C++ development and dependency management into the 21st century and on par with the other development ecosystems.
High Performance Computing (HPC) SDK is a comprehensive toolbox for GPU accelerating HPC modeling and simulation applications. It includes the C, C++, and Fortran compilers, libraries, and analysis tools necessary for developing HPC applications on the NVIDIA platform.
Thrust is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.
Boost is an educational opportunity focused on cutting-edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
Automake is a tool for automatically generating Makefile.in files compliant with the GNU Coding Standards. Automake requires the use of GNU Autoconf.
Cmake is an open-source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
GDB is a debugger, that allows you to see what is going on `inside' another program while it executes or what another program was doing at the moment it crashed.
GCC is a compiler Collection that includes front ends for C, C++, Objective-C, Fortran, Ada, Go, and D, as well as libraries for these languages.
GSL is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There are over 1000 functions in total with an extensive test suite.
OpenGL Extension Wrangler Library (GLEW) is a cross-platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
Libtool is a generic library support script that hides the complexity of using shared libraries behind a consistent, portable interface. To use Libtool, add the new generic library building commands to your Makefile, Makefile.in, or Makefile.am.
Maven is a software project management and comprehension tool. Based on the concept of a project object model (POM), Maven can manage a project's build, reporting and documentation from a central piece of information.
TAU (Tuning And Analysis Utilities) is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements as well as event-based sampling. All C++ language features are supported including templates and namespaces.
Clang is a production quality C, Objective-C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.
OpenCV is a highly optimized library with focus on real-time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Libcu++ is the NVIDIA C++ Standard Library for your entire system. It provides a heterogeneous implementation of the C++ Standard Library that can be used in and between CPU and GPU code.
ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It's widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface that makes it easy to respond to the recognition of phrases of interest.
Oat++ is a light and powerful C++ web framework for highly scalable and resource-efficient web application. It's zero-dependency and easy-portable.
JavaCPP is a program that provides efficient access to native C++ inside Java, not unlike the way some C/C++ compilers interact with assembly language.
Cython is a language that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations such as calling C functions and declaring C types on variables and class attributes.
Spdlog is a very fast, header-only/compiled, C++ logging library.
Infer is a static analysis tool for Java, C++, Objective-C, and C. Infer is written in OCaml.
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Java is a popular programming language and development platform(JDK). It reduces costs, shortens development timeframes, drives innovation, and improves application services. With millions of developers running more than 51 billion Java Virtual Machines worldwide.
The Eclipse Foundation is home to a worldwide community of developers, the Eclipse IDE, Jakarta EE and over 375 open source projects, including runtimes, tools and frameworks for Java and other languages.
Getting Started with Java
Oracle Java certifications from Oracle University
Google Developers Training
Google Developers Certification
Java Tutorial by W3Schools
Building Your First Android App in Java
Getting Started with Java in Visual Studio Code
Google Java Style Guide
AOSP Java Code Style for Contributors
Chromium Java style guide
Get Started with OR-Tools for Java
Getting started with Java Tool Installer task for Azure Pipelines
Gradle User Manual
Java SE contains several tools to assist in program development and debugging, and in the monitoring and troubleshooting of production applications.
JDK Development Tools includes the Java Web Start Tools (javaws) Java Troubleshooting, Profiling, Monitoring and Management Tools (jcmd, jconsole, jmc, jvisualvm); and Java Web Services Tools (schemagen, wsgen, wsimport, xjc).
Android Studio is the official integrated development environment for Google's Android operating system, built on JetBrains' IntelliJ IDEA software and designed specifically for Android development. Availble on Windows, macOS, Linux, Chrome OS.
IntelliJ IDEA is an IDE for Java, but it also understands and provides intelligent coding assistance for a large variety of other languages such as Kotlin, SQL, JPQL, HTML, JavaScript, etc., even if the language expression is injected into a String literal in your Java code.
NetBeans is an IDE provides Java developers with all the tools needed to create professional desktop, mobile and enterprise applications. Creating, Editing, and Refactoring. The IDE provides wizards and templates to let you create Java EE, Java SE, and Java ME applications.
Java Design Patterns is a collection of the best formalized practices a programmer can use to solve common problems when designing an application or system.
Elasticsearch is a distributed RESTful search engine built for the cloud written in Java.
RxJava is a Java VM implementation of Reactive Extensions: a library for composing asynchronous and event-based programs by using observable sequences. It extends the observer pattern to support sequences of data/events and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety and concurrent data structures.
Guava is a set of core Java libraries from Google that includes new collection types (such as multimap and multiset), immutable collections, a graph library, and utilities for concurrency, I/O, hashing, caching, primitives, strings, and more! It is widely used on most Java projects within Google, and widely used by many other companies as well.
okhttp is a HTTP client for Java and Kotlin developed by Square.
Retrofit is a type-safe HTTP client for Android and Java develped by Square.
LeakCanary is a memory leak detection library for Android develped by Square.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities with elegant and fluent APIs in Java and Scala.
Fastjson is a Java library that can be used to convert Java Objects into their JSON representation. It can also be used to convert a JSON string to an equivalent Java object.
libGDX is a cross-platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.
Jenkins is the leading open-source automation server. Built with Java, it provides over 1700 plugins to support automating virtually anything, so that humans can actually spend their time doing things machines cannot.
DBeaver is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports any database which has JDBC driver (which basically means - ANY database). EE version also supports non-JDBC datasources (MongoDB, Cassandra, Redis, DynamoDB, etc).
Redisson is a Redis Java client with features of In-Memory Data Grid. Over 50 Redis based Java objects and services: Set, Multimap, SortedSet, Map, List, Queue, Deque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Tomcat, Scheduler, JCache API, Hibernate, MyBatis, RPC, and local cache.
GraalVM is a universal virtual machine for running applications written in JavaScript, Python, Ruby, R, JVM-based languages like Java, Scala, Clojure, Kotlin, and LLVM-based languages such as C and C++.
Gradle is a build automation tool for multi-language software development. From mobile apps to microservices, from small startups to big enterprises, Gradle helps teams build, automate and deliver better software, faster. Write in Java, C++, Python or your language of choice.
Apache Groovy is a powerful, optionally typed and dynamic language, with static-typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming.
JaCoCo is a free code coverage library for Java, which has been created by the EclEmma team based on the lessons learned from using and integration existing libraries for many years.
Apache JMeter is used to test performance both on static and dynamic resources, Web dynamic applications. It also used to simulate a heavy load on a server, group of servers, network or object to test its strength or to analyze overall performance under different load types.
Junit is a simple framework to write repeatable tests. It is an instance of the xUnit architecture for unit testing frameworks.
Mockito is the most popular Mocking framework for unit tests written in Java.
SpotBugs is a program which uses static analysis to look for bugs in Java code.
SpringBoot is a great tool that helps you to create Spring-powered, production-grade applications and services with absolute minimum fuss. It takes an opinionated view of the Spring platform so that new and existing users can quickly get to the bits they need.
YourKit is a technology leader, creator of the most innovative and intelligent tools for profiling Java & .NET applications.
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Python is an interpreted, high-level programming language. Python is used heavily in the fields of Data Science and Machine Learning.
Python Developer's Guide is a comprehensive resource for contributing to Python – for both new and experienced contributors. It is maintained by the same community that maintains Python.
Azure Functions Python developer guide is an introduction to developing Azure Functions using Python. The content below assumes that you've already read the Azure Functions developers guide.
CheckiO is a programming learning platform and a gamified website that teaches Python through solving code challenges and competing for the most elegant and creative solutions.
파이썬 연구소
PCEP – Certified Entry-Level Python Programmer certification
PCAP – Certified Associate in Python Programming certification
PCPP – Certified Professional in Python Programming 1 certification
PCPP – Certified Professional in Python Programming 2
MTA: Introduction to Programming Using Python Certification
Getting Started with Python in Visual Studio Code
Google's Python Style Guide
Google's Python Education Class
Real Python
The Python Open Source Computer Science Degree by Forrest Knight
Intro to Python for Data Science
Intro to Python by W3schools
Codecademy's Python 3 course
Learn Python with Online Courses and Classes from edX
Python Courses Online from Coursera
Python Package Index (PyPI) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community.
PyCharm is the best IDE I've ever used. With PyCharm, you can access the command line, connect to a database, create a virtual environment, and manage your version control system all in one place, saving time by avoiding constantly switching between windows.
Python Tools for Visual Studio(PTVS) is a free, open source plugin that turns Visual Studio into a Python IDE. It supports editing, browsing, IntelliSense, mixed Python/C++ debugging, remote Linux/MacOS debugging, profiling, IPython, and web development with Django and other frameworks.
Pylance is an extension that works alongside Python in Visual Studio Code to provide performant language support. Under the hood, Pylance is powered by Pyright, Microsoft's static type checking tool.
Pyright is a fast type checker meant for large Python source bases. It can run in a “watch” mode and performs fast incremental updates when files are modified.
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.
Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.
Web2py is an open-source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.
AWS Chalice is a framework for writing serverless apps in python. It allows you to quickly create and deploy applications that use AWS Lambda.
Tornado is a Python web framework and asynchronous networking library. Tornado uses a non-blocking network I/O, which can scale to tens of thousands of open connections.
HTTPie is a command line HTTP client that makes CLI interaction with web services as easy as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.
Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.
Sentry is a service that helps you monitor and fix crashes in realtime. The server is in Python, but it contains a full API for sending events from any language, in any application.
Pipenv is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world.
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library.
CherryPy is a minimalist Python object-oriented HTTP web framework.
Sanic is a Python 3.6+ web server and web framework that's written to go fast.
Pyramid is a small and fast open source Python web framework. It makes real-world web application development and deployment more fun and more productive.
TurboGears is a hybrid web framework able to act both as a Full Stack framework or as a Microframework.
Falcon is a reliable, high-performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.
Neural Network Intelligence(NNI) is an open source AutoML toolkit for automate machine learning lifecycle, including Feature Engineering, Neural Architecture Search, Model Compression and Hyperparameter Tuning.
Dash is a popular Python framework for building ML & data science web apps for Python, R, Julia, and Jupyter.
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built-in.
Locust is an easy to use, scriptable and scalable performance testing tool.
spaCy is a library for advanced Natural Language Processing in Python and Cython.
NumPy is the fundamental package needed for scientific computing with Python.
Pillow is a friendly PIL(Python Imaging Library) fork.
IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history.
GraphLab Create is a Python library, backed by a C++ engine, for quickly building large-scale, high-performance machine learning models.
Pandas is a fast, powerful, and easy to use open source data structrures, data analysis and manipulation tool, built on top of the Python programming language.
PuLP is an Linear Programming modeler written in python. PuLP can generate LP files and call on use highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI, to solve these linear problems.
Matplotlib is a 2D plotting library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
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Scala is a combination of object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.
Scala Style Guide
Databricks Scala Style Guide
Data Science using Scala and Spark on Azure
Creating a Scala Maven application for Apache Spark in HDInsight using IntelliJ
Intro to Spark DataFrames using Scala with Azure Databricks
Using Scala to Program AWS Glue ETL Scripts
Using Flink Scala shell with Amazon EMR clusters
AWS EMR and Spark 2 using Scala from Udemy
Using the Google Cloud Storage connector with Apache Spark
Write and run Spark Scala jobs on Cloud Dataproc for Google Cloud
Scala Courses and Certifications from edX
Scala Courses from Coursera
Top Scala Courses from Udemy
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Play Framework is a web framework combines productivity and performance making it easy to build scalable web applications with Java and Scala.
Dotty is a research compiler that will become Scala 3.
AWScala is a tool that enables Scala developers to easily work with Amazon Web Services in the Scala way.
Scala.js is a compiler that converts Scala to JavaScript.
Polynote is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega.
Scala Native is an optimizing ahead-of-time compiler and lightweight managed runtime designed specifically for Scala.
Gitbucket is a Git platform powered by Scala with easy installation, high extensibility & GitHub API compatibility.
Finagle is a fault tolerant, protocol-agnostic RPC system
Gatling is a load test tool. It officially supports HTTP, WebSocket, Server-Sent-Events and JMS.
Scalatra is a tiny Scala high-performance, async web framework, inspired by Sinatra.
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R is an open source software environment for statistical computing and graphics. It compiles and runs on a wide variety of platforms such as Windows and MacOS.
An Introduction to R
Google's R Style Guide
R developer's guide to Azure
Running R at Scale on Google Compute Engine
Running R on AWS
RStudio Server Pro for AWS
Learn R by Codecademy
Learn R Programming with Online Courses and Lessons by edX
R Language Courses by Coursera
Learn R For Data Science by Udacity
RStudio is an integrated development environment for R and Python, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
Shiny is a newer package from RStudio that makes it incredibly easy to build interactive web applications with R.
Rmarkdown is a package helps you create dynamic analysis documents that combine code, rendered output (such as figures), and prose.
Rplugin is R Language supported plugin for the IntelliJ IDE.
Plotly is an R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js.
Metaflow is a Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.
LightGBM is a gradient boosting framework that uses tree based learning algorithms, used for ranking, classification and many other machine learning tasks.
Dash is a Python framework for building analytical web applications in Python, R, Julia, and Jupyter.
MLR is Machine Learning in R.
ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. ML workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (Tensorflow, PyTorch, Keras, and MXnet) and dev tools (Jupyter, VS Code, and Tensorboard) perfectly configured, optimized, and integrated.
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Plumber is a tool that allows you to create a web API by merely decorating your existing R source code with special comments.
Drake is an R-focused pipeline toolkit for reproducibility and high-performance computing.
DiagrammeR is a package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into R Markdown documents, integrated with Shiny web apps, converted to other graph formats, or exported as image files.
Knitr is a general-purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.
Broom is a tool that converts statistical analysis objects from R into tidy format.
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Julia is a high-level, high-performance dynamic language for technical computing. Julia programs compile to efficient native code for multiple platforms via LLVM.
JuliaHub contains over 4,000 Julia packages for use by the community.
Julia Observer
Julia Manual
JuliaLang Essentials
Julia Style Guide
Julia By Example
JuliaLang Gitter
DataFrames Tutorial using Jupyter Notebooks
Julia Academy
Julia Meetup groups
Julia on Microsoft Azure
JuliaPro is a free and fast way to setup Julia for individual researchers, engineers, scientists, quants, traders, economists, students and others. Julia developers can build better software quicker and easier while benefiting from Julia's unparalleled high performance. It includes 2600+ open source packages or from a curated list of 250+ JuliaPro packages. Curated packages are tested, documented and supported by Julia Computing.
Juno is a powerful, free IDE based on Atom for the Julia language.
Debugger.jl is the Julia debuggin tool.
Profile (Stdlib) is a module provides tools to help developers improve the performance of their code. When used, it takes measurements on running code, and produces output that helps you understand how much time is spent on individual line's.
Revise.jl allows you to modify code and use the changes without restarting Julia. With Revise, you can be in the middle of a session and then update packages, switch git branches, and/or edit the source code in the editor of your choice; any changes will typically be incorporated into the very next command you issue from the REPL. This can save you the overhead of restarting Julia, loading packages, and waiting for code to JIT-compile.
JuliaGPU is a Github organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.
IJulia.jl is the Julia kernel for Jupyter.
AWS.jl is a Julia interface for Amazon Web Services.
CUDA.jl is a package for the main programming interface for working with NVIDIA CUDA GPUs using Julia. It features a user-friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.
XLA.jl is a package for compiling Julia to XLA for Tensor Processing Unit(TPU).
Nanosoldier.jl is a package for running JuliaCI services on MIT's Nanosoldier cluster.
Julia for VSCode is a powerful extension for the Julia language.
JuMP.jl is a domain-specific modeling language for mathematical optimization embedded in Julia.
Optim.jl is a univariate and multivariate optimization in Julia.
RCall.jl is a package that allows you to call R functions from Julia.
JavaCall.jl is a package that allows you to call Java functions from Julia.
PyCall.jl is a package that allows you to call Python functions from Julia.
MXNet.jl is the Apache MXNet Julia package. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia.
Knet is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia.
Distributions.jl is a Julia package for probability distributions and associated functions.
DataFrames.jl is a tool for working with tabular data in Julia.
Flux.jl is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support.
IRTools.jl is a simple and flexible IR format, expressive enough to work with both lowered and typed Julia code, as well as external IRs.
Cassette.jl is a Julia package that provides a mechanism for dynamically injecting code transformation passes into Julia's just-in-time (JIT) compilation cycle, enabling post hoc analysis and modification of "Cassette-unaware" Julia programs without requiring manual source annotation or refactoring of the target code.
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