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21 Lessons, Get Started Building with Generative AI
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Learn how to develop, deploy and iterate on production-grade ML applications.
12 Weeks, 24 Lessons, AI for All!
10 Weeks, 20 Lessons, Data Science for All!
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Neural Networks: Zero to Hero
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
💿 Free software that works great, and also happens to be open-source Python.
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
Flax is a neural network library for JAX that is designed for flexibility.
A course in reinforcement learning in the wild
Portfolio and risk analytics in Python
Repo for the Deep Reinforcement Learning Nanodegree program
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse m…
A high performance implementation of HDBSCAN clustering.
A python tutorial on bayesian modeling techniques (PyMC3)
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Handout for the tutorial "Creating publication-quality figures with matplotlib"
Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Pape…
Simulation framework for accelerating research in Private Federated Learning
The open source initiative for anonymized, elite-level athletic motion capture data. Run by Driveline Baseball.
ME 539 - Introduction to Scientific Machine Learning
Altair extension for saving charts in a variety of formats.
