Machine Learning · GitHub

A Full Stack ML (Machine Learning) Roadmap involves learning the necessary skills and technologies to become proficient in all aspects of machine learning, including data collection and preprocessing, model development, deployment, and maintenance.

Convolutional neural network model for video classification trained on the Kinetics dataset.

Fast and accurate AI powered file content types detection

• Available as a command line tool written in Rust, a Python API, and additional bindings for Rust, JavaScript/TypeScript (with an experimental npm package, which powers our web demo), and GoLang (WIP).
• Trained and evaluated on a dataset of ~100M files across 200+ content types.

Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)

An android library that uses technologies like artificial Intelligence, machine learning, and deep learning to make developers understand the content that they are displaying in their app.

[CVPR 2024 Highlight] Official PyTorch implementation of SpatialTracker: Tracking Any 2D Pixels in 3D Space

Multi-class text classifier that routes consumer complaints to the right product category using TF-IDF, LinearSVC and a CLI router.

Predicting loan default on 1.35M Lending Club loans with 6 ML models: class imbalance, ROC-AUC and data leakage explained.

Bike-share demand forecasting: RMSE compared across raw, encoded, scaled and engineered features.

• hour - Hour of day (0 to 23) from timestamp
• day_of_week - Day of week (0 is Monday)

A list of all named GANs!

Multi-Scale Neural Audio Codec (SNAC) compresses audio into discrete codes at a low bitrate

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.

Build voice agents with open-source models

:satellite: Simple and ready-to-use tutorials for TensorFlow