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.

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

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。

Simple tutorials using Google's TensorFlow Framework

Tacotron 2 - PyTorch implementation with faster-than-realtime inference

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

TensorFlow Basic Tutorial Labs

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

Probabilistic reasoning and statistical analysis in TensorFlow

🛠 All-in-one web-based IDE specialized for machine learning and data science.

An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) fo…

Beaker Extensions for Jupyter Notebook

subpixel: A subpixel convnet for super resolution with Tensorflow

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

Demo of running NNs across different frameworks

Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch

Experiments with Deep Learning

Demonstrations of Magenta Models

Computations and statistics on manifolds with geometric structures.

Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

Python Helper library for Jupyter Notebooks

Dynamic seq2seq in TensorFlow, step by step

Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

Python implementation of KNN and DTW classification algorithm

Bayesian learning and inference for state space models

PyTorch implementation of GAN-based text-to-speech synthesis and voice conversion (VC)

PaCMAP: Large-scale Dimension Reduction Technique Preserving Both Global and Local Structure

apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.rea…

Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟