🦜🔗 Build context-aware reasoning applications

A latent text-to-image diffusion model

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

🔊 Text-Prompted Generative Audio Model

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 ;)

Python programs, usually short, of considerable difficulty, to perfect particular skills.

The fastai book, published as Jupyter Notebooks

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…

Data and code behind the articles and graphics at FiveThirtyEight

A collection of various deep learning architectures, models, and tips

Code for Machine Learning for Algorithmic Trading, 2nd edition.

A multi-voice TTS system trained with an emphasis on quality

面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版

A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.

Foundational Models for State-of-the-Art Speech and Text Translation

Solve puzzles. Learn CUDA.

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

Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion

Code samples used on cloud.google.com

Visualizations for machine learning datasets

A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

The Udacity open source self-driving car project

An adversarial example library for constructing attacks, building defenses, and benchmarking both

Portfolio and risk analytics in Python

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

The 3rd edition of course.fast.ai

Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models