Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Google Research

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.

Course Files for Complete Python 3 Bootcamp Course on Udemy

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

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

🤖 💬 Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

Lab Materials for MIT 6.S191: Introduction to Deep Learning

"Probabilistic Machine Learning" - a book series by Kevin Murphy

Text and supporting code for Think Stats, 2nd Edition

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

A computer science textbook

🔥 Machine Learning Notebooks

Text and code for the second edition of Think Bayes, by Allen Downey.

A few exercises for use at events.

Official Repository for Code associated with 'Practical Natural Language Processing' book by O'Reilly Media

Official code repo for the O'Reilly Book - Practical Deep Learning for Cloud, Mobile & Edge

[Book-2021] Practical MLOps O'Reilly Book

Complete implementations from "Algorithms for Modern Hardware"

18.335 - Introduction to Numerical Methods course

Jupyter Notebooks derived from Allen Downey's book Think Bayes.

Race-line calculation of a DeepRacer track

18.369/8.315 - Mathematical Methods in Nanophotonics course

An implementation of 2021 paper by Geoffrey Hinton: "How to represent part-whole hierarchies in a neural network" in Pytorch.

basic RNN model in tensorflow