tensorflow ml nlp

A natural language processing book starting with TensorFlow 2 and machine learning has been released. For the second book, please refer to the link below.

Second book link: https://github.com/NLP-kr/tensorflow-ml-nlp-tf2

NLPBOOK

Natural language processing starting with TensorFlow and machine learning (from logistic regression to transformer chatbot)

Introduction

This is a repository that collects natural language processing examples included in the book.

This repository will be more helpful if you use it in conjunction with a natural language processing book that starts with TensorFlow and machine learning.

Required Libraries (Reuquirements)

1. Python3
2. Tensorflow==1.10
3. numpy
4. pandas
5. matplot

Installation method (Environments)

 pip install -r requirements.txt

If the installation does not proceed properly, please install python 3.6 and proceed.

 conda install python=3.6

CUDA installation when using GPU

• If you use a GPU, you must install a CUDA version compatible with TensorFlow.
• Currently, this project has been implemented and tested to run on tensorflow==1.10 version.
• For tensorflow-gpu==1.10 please install CUDA 9.0 .

Table of Contents

Preparatory stage - This chapter provides background on natural language processing and preparation for development.

1. Entering
2. Preparing for natural language processing development
3. Natural Language Processing Overview

Natural Language Processing Basics - This is a practice chapter on basic models in natural language processing.

4. Text classification
5. Text Similarity

Advanced Natural Language Processing - This is a practice chapter on more advanced natural language processing through chatbot models.

6. Create a chatbot

Inquiries

Pull requests are always welcome. If you have any problems, bugs, or questions, please leave a message in the issue.

**Before checking the issue, please look at the document on Wiki first and leave a comment on the issue!

Authors

ChangWookJun / @changwookjun ([email protected])
Taekyoon / @taekyoon ([email protected])
JungHyun Cho / @JungHyunCho ([email protected])