Deep Learning
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A curated list of libraries, tutorials, and resources for implementing machine learning in the Ruby programming language.
PyGAD is a Python library for building genetic algorithms and optimizing machine learning models with Keras and PyTorch support.
A curated list of academic papers and resources for image and video inpainting techniques.
A simple wrapper that combines Keras and Hyperopt for convenient hyperparameter optimization in deep learning models.
A curated list of awesome libraries, projects, tutorials, and resources for the JAX machine learning ecosystem.
A curated list of deep learning implementations and resources for biological research, with a focus on genomics.
A Python library for constructing differentiable convex optimization layers in PyTorch, JAX, and MLX using CVXPY.
A JAX-based library providing numerical differential equation solvers for ODEs, SDEs, and CDEs with autodifferentiation and GPU support.
An open-source library of high-performance, high-quality denoising filters for ray-traced images using deep learning.
A web/desktop application for collaborative labeling and annotation of images, text, audio, documents, and other data types.
A platform for developing AI bots that play Doom using visual information, designed for reinforcement learning research.
A deep learning-based facial detection library for Python with facial landmark extraction.
A TensorFlow library for building, training, and deploying recommender system models with Keras.
An unsupervised learning framework for depth and ego-motion estimation from monocular videos using TensorFlow.
A framework for running deep neural network models directly in web browsers using ONNX format with WebGPU, WebGL, and WebAssembly backends.
A curated collection of research papers and software for explainable graph machine learning and reasoning.
A deep learning library in Rust featuring shape-checked tensors and neural networks with compile-time safety.
A deep learning library for Rust featuring shape-checked tensors and neural networks with compile-time safety.
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A deep learning-based edge detection algorithm using holistically-nested fully convolutional neural networks.
:unlock: Lip Reading - Cross Audio-Visual Recognition using 3D Architectures
A minimal benchmark comparing scalability, speed, and accuracy of popular open-source machine learning libraries for binary classification.
A visual workflow-based AI deployment framework for multi-platform and multi-backend inference, supporting large models and edge devices.
An end-to-end deep learning system for reconstructing complete 3D scenes (geometry and semantics) from posed 2D images.
A self-contained machine learning and natural language processing library written in pure Go with a dynamic computational graph.
A deep reinforcement learning framework for financial portfolio management with policy gradient optimization and backtesting tools.
A deep learning model for protein sequence design that generates amino acid sequences for given protein backbones.
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
A collection of interactive machine learning experiments with Jupyter notebooks for training and browser demos for visualization.
A curated collection of high-quality deep learning resources, including courses, books, papers, libraries, and datasets.
A library for creating TensorFlow models that handle structured data with dynamic computation graphs using dynamic batching.
An abstraction layer over MetalPerformanceShaders for crafting and running fast neural networks on iOS using TensorFlow models.
An open-source Python toolkit providing a comprehensive collection of algorithms for interpreting and explaining machine learning models and datasets.
An end-to-end 3D object detection network that uses deep point set networks and Hough voting to directly detect objects in point clouds.
A collection of models, callbacks, and datasets to extend PyTorch Lightning for applied AI/ML research and production.
An easy-to-use, state-of-the-art named-entity recognition (NER) tool based on neural networks.
A lightweight library providing PyTorch training tools and utilities to simplify and standardize training loops.
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