These are my personal notes taken while following the Udacity Generative AI Nanodegree.
The Nanodegree asssumes basic data analysis skills with data science python libraries and databases, and has 4 modules that build up on those skills; each module has its corresponding folder in this repository with its guide Markdown file:
01_Fundamentals_GenAI
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02_LLMs
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03_ComputerVision
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04_BuildingSolutions
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Additionally, it is necessary to submit and pass some projects to get the certification:
Finally, also check some of my personal guides on related tools:
mxagar/tool_guides/hugging_face
mxagar/tool_guides/langchain
mxagar/tool_guides/llms
mxagar/nlp_guide
mxagar/computer_vision_udacity/CVND_Advanced_CV_and_DL.md
mxagar/deep_learning_udacity/DLND_RNNs.md
A regular python environment with the usual data science packages should suffice (i.e., scikit-learn, pandas, matplotlib, etc.); any special/additional packages and their installation commands are introduced in the guides. A recipe to set up a conda environment with my current packages is the following:
conda create --name ds pip python=3.10
conda activate ds
pip install -r requirements.txt
Many of the contents in this repository were created following the Udacity Generative AI Nanodegree.
Mikel Sagardia, 2024.
No guarantees.