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1. fau-masters-collected-works-cgarbin/writing-good-jupyter-notebooks fau-masters-collected-works-cgarbin/writing-good-jupyter-notebooks Public

   Writing good Jupyter notebooks: logically organized, clearly documented decisions and assumptions, easy-to-understand code, flexible (easy to modify) code, resilient (hard to break) code

   Jupyter Notebook 3

2. fau-masters-collected-works-cgarbin/gpt-all-local fau-masters-collected-works-cgarbin/gpt-all-local Public

   A "chat with your data" example: using a large language models (LLM) to interact with our own (local) data. Everything is local: the embedding model, the LLM, the vector database. This is an exampl…

   Python 17 1

3. fau-masters-collected-works-cgarbin/llm-github-issues fau-masters-collected-works-cgarbin/llm-github-issues Public

   Summarizing with LLMs: Using an LLM to understand GitHub issues without reading each post in detail.

   Python 4

4. fau-masters-collected-works-cgarbin/ieee-icmla-2019-data-science-tutorial fau-masters-collected-works-cgarbin/ieee-icmla-2019-data-science-tutorial Public

   IEEE ICMLA 2019 Data Science Tutorial - using data to answer questions

   Jupyter Notebook 9 5

5. fau-masters-collected-works-cgarbin/dropout-vs-batch-normalization fau-masters-collected-works-cgarbin/dropout-vs-batch-normalization Public

   Dropout vs. batch normalization: effect on accuracy, training and inference times - code for the paper

   TeX 8 1

6. fau-masters-collected-works-cgarbin/machine-learning-but-not-understanding fau-masters-collected-works-cgarbin/machine-learning-but-not-understanding Public

   Are machines "learning" anything? This repository explores some of the concepts from the book "Artificial Intelligence, a guide for thinking humans", by Melanie Mitchell.

   Jupyter Notebook