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realistic-ssl-evaluation

Open source release of the evaluation benchmark suite described in "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"

recommenders

Best Practices on Recommendation Systems

regularized_autoencoders-rae-

rlexample

Some basic examples of playing with RL

roboprep

satellite_imagery_analysis

Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

scaledyolov4

Scaled-YOLOv4: Scaling Cross Stage Partial Network

scikit-multilearn

A scikit-learn based module for multi-label et. al. classification

scrcpy

Display and control your Android device

segment-anything

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.

self-attention-gan

self-instruct

Aligning pretrained language models with instruction data generated by themselves.

semantic-kernel

Integrate cutting-edge LLM technology quickly and easily into your apps

show-attend-and-tell

TensorFlow Implementation of "Show, Attend and Tell"

sngan_projection

GANs with spectral normalization and projection discriminator

sort

Simple, online, and realtime tracking of multiple objects in a video sequence.

spotifygenerateplaylist

stable-diffusion

A latent text-to-image diffusion model

stanford_alpaca

Code and documentation to train Stanford's Alpaca models, and generate the data.

streamlit

Streamlit — The fastest way to build data apps in Python

stylegan

StyleGAN - Official TensorFlow Implementation

stylegan.pytorch

A PyTorch implementation for StyleGAN with full features.

stylegan2

StyleGAN2 - Official TensorFlow Implementation

stylegan2-ada-pytorch

StyleGAN2-ADA - Official PyTorch implementation

stylespeech

Official implementation of Meta-StyleSpeech and StyleSpeech

svoice

We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.

tacotron2

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

tech-interview-handbook

💯 Materials to help you rock your next coding interview

tensor2tensor

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

tensorflow-without-a-phd

A crash course in six episodes for software developers who want to become machine learning practitioners.