Deep Learning
Showing 36 of 402 projects
A TensorFlow library for building, training, and deploying recommender system models with Keras.
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 JAX-based library providing numerical differential equation solvers for ODEs, SDEs, and CDEs with autodifferentiation and GPU support.
A deep learning-based facial detection library for Python with facial landmark extraction.
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A deep learning-based edge detection algorithm using holistically-nested fully convolutional neural networks.
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.
A minimal benchmark comparing scalability, speed, and accuracy of popular open-source machine learning libraries for binary classification.
An end-to-end deep learning system for reconstructing complete 3D scenes (geometry and semantics) from posed 2D images.
A deep reinforcement learning framework for financial portfolio management with policy gradient optimization and backtesting tools.
A self-contained machine learning and natural language processing library written in pure Go with a dynamic computational graph.
A library for creating TensorFlow models that handle structured data with dynamic computation graphs using dynamic batching.
A collection of interactive machine learning experiments with Jupyter notebooks for training and browser demos for visualization.
An abstraction layer over MetalPerformanceShaders for crafting and running fast neural networks on iOS using TensorFlow models.
A curated collection of high-quality deep learning resources, including courses, books, papers, libraries, and datasets.
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
A visual workflow-based AI deployment framework for multi-platform and multi-backend inference, supporting large models and edge devices.
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.
A deep learning model for protein sequence design that generates amino acid sequences for given protein backbones.
A neural network library for Elixir built on Nx, providing functional, model creation, and training APIs for deep learning.
A curated checklist of state-of-the-art research materials (datasets, papers, code) for interaction-aware trajectory prediction.
A deep learning pipeline for 3D object detection from RGB-D data by combining 2D detectors with PointNet-based 3D processing.
A Python library for offline deep reinforcement learning with support for state-of-the-art algorithms and user-friendly APIs.
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
A Python library for automated hyperparameter optimization and model evaluation with TensorFlow, Keras, and PyTorch.
A language for distributed deep learning that simplifies model parallelism by specifying tensor computations across hardware meshes.
A Python package for generating synthetic tabular and time-series data using state-of-the-art generative models like GANs and Gaussian Mixtures.
A Python library for deep probabilistic modeling and analysis of single-cell and spatial omics data.
A deprecated repository for community-contributed Keras extensions like layers, activations, and loss functions.
Elephas is a Keras extension for distributed deep learning on Apache Spark, enabling data-parallel training at scale.
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