{"id":18339885,"url":"https://github.com/mxagar/generative_ai_udacity","last_synced_at":"2025-07-08T07:34:06.682Z","repository":{"id":228992938,"uuid":"775474400","full_name":"mxagar/generative_ai_udacity","owner":"mxagar","description":"My personal notes, code and projects of the Udacity Generative AI Nanodegree. ","archived":false,"fork":false,"pushed_at":"2025-01-24T17:22:59.000Z","size":21263,"stargazers_count":3,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-24T18:25:12.127Z","etag":null,"topics":["ai","computer-vision","data-science","diffusion-models","genai","generative-ai","llm","llm-finetuning","machine-learning","rag"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mxagar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-03-21T13:08:50.000Z","updated_at":"2025-01-24T17:23:02.000Z","dependencies_parsed_at":"2024-06-12T21:59:04.258Z","dependency_job_id":"cdbaa7ac-de60-45ef-afff-e09618b84453","html_url":"https://github.com/mxagar/generative_ai_udacity","commit_stats":null,"previous_names":["mxagar/generative_ai_udacity"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fgenerative_ai_udacity","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fgenerative_ai_udacity/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fgenerative_ai_udacity/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fgenerative_ai_udacity/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mxagar","download_url":"https://codeload.github.com/mxagar/generative_ai_udacity/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238994285,"owners_count":19564812,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","computer-vision","data-science","diffusion-models","genai","generative-ai","llm","llm-finetuning","machine-learning","rag"],"created_at":"2024-11-05T20:19:42.679Z","updated_at":"2025-07-08T07:34:06.675Z","avatar_url":"https://github.com/mxagar.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Udacity Generative AI Nanodegree: Personal Notes\n\nThese are my personal notes taken while following the [Udacity Generative AI Nanodegree](https://www.udacity.com/course/generative-ai--nd608).\n\nThe 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:\n\n1. Generative AI Fundamentals: [`01_Fundamentals_GenAI`](./01_Fundamentals_GenAI/README.md).\n    - Foundation Models\n    - Fine-Tuning\n2. Large Language Models (LLMs) \u0026 Text Generation: [`02_LLMs`](./02_LLMs/README.md).\n    - Transformers and LLMs\n    - Retrieval Augmented Generation (RAG) Chatbots\n3. Computer Vision and Generative AI: [`03_ComputerVision`](./03_ComputerVision/README.md).\n    - Generative Adversarial Networks (GANs)\n    - Vision Transformers\n    - Diffusion Models\n4. Building Generative AI Solutions: [`04_BuildingSolutions`](./04_BuildingSolutions/README.md).\n    - Vector Databases\n    - LangChain and Agents\n\nAdditionally, it is necessary to submit and pass some projects to get the certification:\n\n- Project 1: Apply Lightweight Fine-Tuning to a Foundation Model: [mxagar/llm_peft_fine_tuning_example](https://github.com/mxagar/llm_peft_fine_tuning_example).\n- Project 2: Build Your Own Custom Chatbot - TBD.\n- Project 3: AI Photo Editing with Inpainting - TBD.\n- Project 4: Personalized Real Estate Agent - TBD.\n\nFinally, also check some of my personal guides on related tools:\n\n- My personal notes on the O'Reilly book [Generative Deep Learning, 2nd Edition, by David Foster](https://github.com/mxagar/generative_ai_book)\n- 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)\n- [HuggingFace Guide: `mxagar/tool_guides/hugging_face`](https://github.com/mxagar/tool_guides/tree/master/hugging_face)\n- [LangChain Guide: `mxagar/tool_guides/langchain`](https://github.com/mxagar/tool_guides/tree/master/langchain)\n- [LLM Tools: `mxagar/tool_guides/llms`](https://github.com/mxagar/tool_guides/tree/master/llms)\n- [NLP Guide: `mxagar/nlp_guide`](https://github.com/mxagar/nlp_guide)\n- [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)\n- [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)\n\n\u003c!--\nFinally, check these additional related courses:\n- [Udacity Course on Small Datasets and Synthetic Data](https://www.udacity.com/course/small-data--cd12528)\n--\u003e\n\n## Setup\n\nA 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:\n\n```bash\n# Create the necessary Python environment\n# NOTE: specific folders might require their own environment\n# and have their own requirements.txt\nconda env create -f conda.yaml\nconda activate genai\n\n# Dependencies\npip-compile requirements.in\npip-sync requirements.txt\n\n# If we need a new dependency,\n# add it to requirements.in \n# (WATCH OUT: try to follow alphabetical order)\n# And then:\npip-compile requirements.in\npip-sync requirements.txt\n\n# When the repository is cloned, initialize and update the submodules \ngit clone https://github.com/mxagar/generative_ai_udacity\ngit submodule update --init --recursive\n```\n\n## Credits\n\nMany of the contents in this repository were created following the [Udacity Generative AI Nanodegree](https://www.udacity.com/course/generative-ai--nd608).\n\nMikel Sagardia, 2024.  \nNo guarantees.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fgenerative_ai_udacity","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmxagar%2Fgenerative_ai_udacity","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fgenerative_ai_udacity/lists"}