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https://github.com/mxagar/generative_ai_udacity

My personal notes, code and projects of the Udacity Generative AI Nanodegree.
https://github.com/mxagar/generative_ai_udacity

ai computer-vision data-science diffusion-models genai generative-ai llm llm-finetuning machine-learning rag

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My personal notes, code and projects of the Udacity Generative AI Nanodegree.

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README 

        
# Udacity Generative AI Nanodegree: Personal Notes



These are my personal notes taken while following the [Udacity Generative AI Nanodegree](https://www.udacity.com/course/generative-ai--nd608).



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:



1. Generative AI Fundamentals: [`01_Fundamentals_GenAI`](./01_Fundamentals_GenAI/README.md).

    - Foundation Models

    - Fine-Tuning

2. Large Language Models (LLMs) & Text Generation: [`02_LLMs`](./02_LLMs/README.md).

    - Transformers and LLMs

    - Retrieval Augmented Generation (RAG) Chatbots

3. Computer Vision and Generative AI: [`03_ComputerVision`](./03_ComputerVision/README.md).

    - Generative Adversarial Networks (GANs)

    - Vision Transformers

    - Diffusion Models

4. Building Generative AI Solutions: [`04_BuildingSolutions`](./04_BuildingSolutions/README.md).

    - Vector Databases

    - LangChain and Agents



Additionally, it is necessary to submit and pass some projects to get the certification:



- Project 1: Apply Lightweight Fine-Tuning to a Foundation Model - TBD.

- Project 2: Build Your Own Custom Chatbot - TBD.

- Project 3: AI Photo Editing with Inpainting - TBD.

- Project 4: Personalized Real Estate Agent - TBD.



Finally, also check some of my personal guides on related tools:



- My personal notes on the O'Reilly book [Generative Deep Learning, 2nd Edition, by David Foster](https://github.com/mxagar/generative_ai_book)

- My personal notes on the O'Reilly book [Natural Language Processing with Transformers, by Lewis Tunstall, Leandro von Werra and Thomas Wolf (O'Reilly)](https://github.com/mxagar/nlp_with_transformers_nbs)

- [HuggingFace Guide: `mxagar/tool_guides/hugging_face`](https://github.com/mxagar/tool_guides/tree/master/hugging_face)

- [LangChain Guide: `mxagar/tool_guides/langchain`](https://github.com/mxagar/tool_guides/tree/master/langchain)

- [LLM Tools: `mxagar/tool_guides/llms`](https://github.com/mxagar/tool_guides/tree/master/llms)

- [NLP Guide: `mxagar/nlp_guide`](https://github.com/mxagar/nlp_guide)

- [Deep Learning Methods for CV and NLP: `mxagar/computer_vision_udacity/CVND_Advanced_CV_and_DL.md`](https://github.com/mxagar/computer_vision_udacity/blob/main/03_Advanced_CV_and_DL/CVND_Advanced_CV_and_DL.md)

- [Deep Learning Methods for NLP: `mxagar/deep_learning_udacity/DLND_RNNs.md`](https://github.com/mxagar/deep_learning_udacity/blob/main/04_RNN/DLND_RNNs.md)



## Setup



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](https://docs.conda.io/en/latest/) environment with my current packages is the following:



```bash

conda create --name ds pip python=3.10

conda activate ds

pip install -r requirements.txt

```



## Interesting Links



- [microsoft/generative-ai-for-beginners](https://github.com/microsoft/generative-ai-for-beginners)

- ...



## Credits



Many of the contents in this repository were created following the [Udacity Generative AI Nanodegree](https://www.udacity.com/course/generative-ai--nd608).



Mikel Sagardia, 2024.  

No guarantees.