Deep Learning
Showing 36 of 937 projects
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 unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
A Python library for offline deep reinforcement learning with support for state-of-the-art algorithms and user-friendly APIs.
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 deep probabilistic modeling and analysis of single-cell and spatial omics data.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
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 automated hyperparameter optimization and model evaluation with TensorFlow, Keras, and PyTorch.
A Deep Learning Python Toolkit for Healthcare Applications.
A language for distributed deep learning that simplifies model parallelism by specifying tensor computations across hardware meshes.
An all-in-one framework for training state-of-the-art computer vision models, covering pretraining, fine-tuning, and distillation.
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.
A high-level Deep Learning API for JVM and Android developers, written in Kotlin and inspired by Keras.
An efficient neural network for semantic segmentation of large-scale 3D point clouds using random sampling.
A general-purpose PyTorch codebase for 3D object detection with state-of-the-art model implementations and multi-dataset support.
A deep learning model for machine comprehension that uses bi-directional attention flow to answer questions about text passages.
Official repository for Big Transfer (BiT) models, providing pre-trained visual representations for efficient transfer learning across computer vision tasks.
MLBox is a powerful Automated Machine Learning python library.
A TensorFlow library for building Graph Neural Networks with support for heterogeneous graphs and scalable data processing.
A Python library for 3D point cloud processing that leverages the scientific Python stack for complex operations with minimal code.
An accelerated machine learning framework for Go, offering a PyTorch/Jax/TensorFlow-like experience with support for CPUs, GPUs, TPUs, and WASM.
A foundational PyTorch library for training deep learning models, serving as the core engine for the OpenMMLab ecosystem.
A lightweight library for building and training graph neural networks using JAX.
A lightweight library for building and training graph neural networks in JAX, providing graph data structures, utilities, and model implementations.
A curated list of awesome links, software libraries, and resources for robotics development.
A desktop application for semi-automatic image annotation using OpenCV's watershed algorithm with manual brush refinement.
An example Android project demonstrating how to build and integrate TensorFlow for object detection using the camera.
A JAX-based library providing reinforcement learning building blocks for implementing agents, supporting both on-policy and off-policy learning.
A deep learning framework for Julia with GPU support and automatic differentiation using dynamic computational graphs.
A deep learning framework for feature learning directly from point clouds using X-Conv operations, achieving state-of-the-art results in classification and segmentation.
A MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications.
An open-source differentiable dense SLAM library for PyTorch, enabling gradient flow from map outputs to sensor inputs.
A fast, modular PyTorch reference implementation for training and evaluating semantic segmentation models.
A Pythonic deep learning framework built on NumPy with optional CUDA acceleration.
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