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10rules-jupyternotebook-guide

Guide for Reproducible Research and Data Science in Jupyter Notebooks

aimss_python_workshop_2023

amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

arcade-nl2code

auth_php

Basic authentication using PHP

awesome-actions

A curated list of awesome actions to use on GitHub

awesome-seml

A curated list of articles that cover the software engineering best practices for building machine learning applications.

bootstrap_code

Bootstrap class lecture with code

ca_project1

Conversational Agents Project 1

camel

🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Language Model Society (NeruIPS'2023) https://www.camel-ai.org

chapyter_nbextension

Chapyter: ChatGPT Code Interpreter in Jupyter Notebooks

chisq_test_wrapper

A friendly, automated chi-square test function which takes care of post-hoc tests and multiple comparisons.

clean-code-ml

:bathtub: Clean Code concepts adapted for machine learning and data science. Now a free video series 😎 https://bit.ly/2yGDyqT

cmput402-code-cov

Repo for learning to integrate a code coverage tool into continuous integration pipeline.

cmput501

Code Comment Inconsistency, Software Bugs, and Comment Size.

cmu-17-214.github.io

cnn_crawler

code_review

codellama_meta

Inference code for CodeLlama models

datascienceproblems

A repository containing the Jupyter notebook code generation benchmark.

debuggingbook_zeller

Project page for "The Debugging Book"

deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

dl_design_smells

ECE720 Course Project 2022

draft_llm_commits

graph_friendsdontletfriends

Friends don't let friends make certain types of data visualization - What are they and why are they bad.

hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow

Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron

hands_on_ml_book

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

how-to-cheat-in-computer-science-101