A latent text-to-image diffusion model

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

Google Research

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

⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

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

Companion webpage to the book "Mathematics For Machine Learning"

Natural Language Processing Tutorial for Deep Learning Researchers

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

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

Public facing notes page

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

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

A collection of pre-trained, state-of-the-art models in the ONNX format

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

Build your neural network easy and fast, 莫烦Python中文教学

Image restoration with neural networks but without learning.

A scikit-learn compatible neural network library that wraps PyTorch

Silero Models: pre-trained text-to-speech models made embarrassingly simple

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

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

Reference models and tools for Cloud TPUs.

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Acceptance rates for the major AI conferences

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

Go to https://github.com/pytorch/tutorials - this repo is deprecated and no longer maintained

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

Plain python implementations of basic machine learning algorithms

This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.