A latent text-to-image diffusion model

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Learn how to design, develop, deploy and iterate on production-grade ML applications.

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

🔊 Text-Prompted Generative Audio Model

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

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 ;)

A game theoretic approach to explain the output of any machine learning model.

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

The fastai book, published as Jupyter Notebooks

A simple screen parsing tool towards pure vision based GUI agent

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

📡 Simple and ready-to-use tutorials for TensorFlow

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

High-Resolution Image Synthesis with Latent Diffusion Models

PRML algorithms implemented in Python

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

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

OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image genera…

A unified framework for 3D content generation.

Simple tutorials using Google's TensorFlow Framework

T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis

Book about interpretable machine learning

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

Data augmentation for NLP

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

Plain python implementations of basic machine learning algorithms

A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks