Christian Garbin CS master's and Ph.D. collected works
Work created during FAU's computer science master's and Ph.D. (data science, machine learning, ...)
United States of America
Pinned Repositories
cap5768-introduction-to-data-science
Data science with Pandas and NumPy: EDA, binning, distribution functions, simulations, regression analysis
datasheet-for-dataset-template
Template for datasheet for datasets
dropout-vs-batch-normalization
Dropout vs. batch normalization: effect on accuracy, training and inference times - code for the paper
gpt-all-local
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 example of retrieval-augmented generation (RAG): we find relevant sections from our documents and pass it to the LLM as part of the prompt (see pics).
ieee-icmla-2019-data-science-tutorial
IEEE ICMLA 2019 Data Science Tutorial - using data to answer questions
llm-github-issues
Summarizing with LLMs: Using an LLM to understand GitHub issues without reading each post in detail.
machine-learning-but-not-understanding
Are machines "learning" anything? This repository explores some of the concepts from the book "Artificial Intelligence, a guide for thinking humans", by Melanie Mitchell.
model-card-template
Template for model cards
shap-experiments-image-classification
Exploring SHAP feature attribution for image classification
writing-good-jupyter-notebooks
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
Christian Garbin CS master's and Ph.D. collected works's Repositories
fau-masters-collected-works-cgarbin/datasheet-for-dataset-template
Template for datasheet for datasets
fau-masters-collected-works-cgarbin/gpt-all-local
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 example of retrieval-augmented generation (RAG): we find relevant sections from our documents and pass it to the LLM as part of the prompt (see pics).
fau-masters-collected-works-cgarbin/model-card-template
Template for model cards
fau-masters-collected-works-cgarbin/cap5768-introduction-to-data-science
Data science with Pandas and NumPy: EDA, binning, distribution functions, simulations, regression analysis
fau-masters-collected-works-cgarbin/ieee-icmla-2019-data-science-tutorial
IEEE ICMLA 2019 Data Science Tutorial - using data to answer questions
fau-masters-collected-works-cgarbin/dropout-vs-batch-normalization
Dropout vs. batch normalization: effect on accuracy, training and inference times - code for the paper
fau-masters-collected-works-cgarbin/llm-github-issues
Summarizing with LLMs: Using an LLM to understand GitHub issues without reading each post in detail.
fau-masters-collected-works-cgarbin/shap-experiments-image-classification
Exploring SHAP feature attribution for image classification
fau-masters-collected-works-cgarbin/writing-good-jupyter-notebooks
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
fau-masters-collected-works-cgarbin/llm-comparison-tool
A tool to compare multiple large language models (LLMs) side by side
fau-masters-collected-works-cgarbin/autogen-experiments
Experiments with LLM agents using Microsoft's AutoGen
fau-masters-collected-works-cgarbin/cap6618-computer-vision
Computer vision using traditional classifiers, neural networks, and transfer learning
fau-masters-collected-works-cgarbin/cap6619-deep-learning
CAP6619 Deep Learning FAU CS master's Fall 2018
fau-masters-collected-works-cgarbin/dataset-visualization-google-facets-streamlit
Exploring data visualization with Facets and Streamlit
fau-masters-collected-works-cgarbin/decision-threshold-effect-on-accuracy
What is "accuracy"? The effect of changing the decision threshold on a model's accuracy.
fau-masters-collected-works-cgarbin/machine-learning-but-not-understanding
Are machines "learning" anything? This repository explores some of the concepts from the book "Artificial Intelligence, a guide for thinking humans", by Melanie Mitchell.
fau-masters-collected-works-cgarbin/cap5615-intro-to-neural-networks
CAP5615 Intro to Neural Networks class at FAU, Summer 2018
fau-masters-collected-works-cgarbin/cap6776-information-retrieval
Information retrieval from basic concepts (tokenization, stop word removal, stemming, TF-IDF, etc.)
fau-masters-collected-works-cgarbin/chestx-ray8-datasheet
A datasheet for the ChestX-ray8 dataset, a.k.a. ChestX-ray14
fau-masters-collected-works-cgarbin/chexnet-model-card
A model card for the CheXNet model.
fau-masters-collected-works-cgarbin/chexpert_explorer
A preprocessor and visualizer for CheXpert
fau-masters-collected-works-cgarbin/cot-5930-image-processing
COT-5930 Image processing using MATLAB
fau-masters-collected-works-cgarbin/cot6405-analysis-of-algorithms
COT6405 Analysis of algorithms Spring 2020
fau-masters-collected-works-cgarbin/cot6900-dis-federated-learning
Federated learning: literature review and experiments with the Google sample code
fau-masters-collected-works-cgarbin/cot6930-natural-language-processing
COT6930 Natural Language Processing, Spring 2019
fau-masters-collected-works-cgarbin/obfuscated-c-12-days-of-christmas
Methodically "unobfuscate" the winner of the 1988 International Obfuscated C Contest entry, the incredible code that prints the "twelve days of Christmas" song.
fau-masters-collected-works-cgarbin/regression-no-libraries
Ridge, elastic net, and logistic regressions implemented without using any statistical or machine learning library. All steps are done by hand, using matrix operations as much as possible.