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.

Python Data Science Handbook: full text in Jupyter Notebooks

Google Research

Data and code behind the articles and graphics at FiveThirtyEight

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

this repository accompanies the book "Grokking Deep Learning"

Google Brain AutoML

The Udacity open source self-driving car project

unet for image segmentation

A collection of infrastructure and tools for research in neural network interpretability.

Bayesian optimization in PyTorch

Google Colaboratory Notebooks and Repositories (by @firmai)

Image Segmentation and Object Detection in Pytorch

Dataset to assess the disentanglement properties of unsupervised learning methods

My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

Siamese Mask R-CNN model for one-shot instance segmentation

EGG: Emergence of lanGuage in Games

ENet - A Neural Net Architecture for real time Semantic Segmentation

A minimal implementaion (less than 150 lines of code with visualization) of DCGAN/WGAN in PyTorch with jupyter notebooks

Tutorial for pycaffe, the Python API to the Neural Network framework, Caffe

Instance Segmentation by Deep Coloring

experimenting with differentiable models of morphogenesis 🔬 🦠