Neural Networks
Showing 36 of 382 projects
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.
A curated collection of high-quality deep learning resources, including courses, books, papers, libraries, and datasets.
An abstraction layer over MetalPerformanceShaders for crafting and running fast neural networks on iOS using TensorFlow models.
A deep learning model for protein sequence design that generates amino acid sequences for given protein backbones.
Implementations of memory-augmented neural networks for language modeling, dialogue systems, and question answering tasks.
An easy-to-use, state-of-the-art named-entity recognition (NER) tool based on neural networks.
A neural network library for Elixir built on Nx, providing functional, model creation, and training APIs for deep learning.
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
A language for distributed deep learning that simplifies model parallelism by specifying tensor computations across hardware meshes.
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 deep learning framework for Julia with GPU support and automatic differentiation using dynamic computational graphs.
A JAX-based library providing reinforcement learning building blocks for implementing agents, supporting both on-policy and off-policy learning.
A Pythonic deep learning framework built on NumPy with optional CUDA acceleration.
A deep learning technique for finding semantically-meaningful dense correspondences between images to enable visual attribute transfer.
A technique using Fourier feature mappings to enable neural networks to learn high-frequency functions in low-dimensional domains.
A modular neural network package for Torch providing building blocks for creating and training deep learning models.
A visual debugger for TensorFlow with breakpoints and real-time data visualization during neural network training.
A dedicated OCaml system for scientific and engineering computing, providing n-dimensional arrays, linear algebra, algorithmic differentiation, and neural networks.
Train neural networks with OpenStreetMap data and satellite imagery to classify roads and map features.
A discontinued Python neural network framework designed for fast, flexible experimentation with CPU and GPU backends.
A minimalist neural network library optimized for sparse data and single-machine environments.
A deep learning framework for Julia inspired by Caffe, featuring modular architecture and multiple backends.
A Clojure library for neural networks, regression, and feature learning with GPU acceleration support.
A library for building high-performance custom human pose estimation applications with real-time inference and flexible model development.
A general-purpose machine learning library for Rust, focusing on speed and ease of use with minimal dependencies.
A Java deep learning framework implementing neural networks with GPU acceleration via OpenCL and Aparapi.
A high-level builder API for TensorFlow that enables fluent, chainable neural network construction.
A deep learning JavaScript library built from scratch with PyTorch-like syntax and GPU acceleration via GPU.js.
A GPU-accelerated deep learning library for Python using CUDA via PyCUDA, implementing neural networks with various training methods.
A Python framework for portfolio optimization using deep learning to allocate investment weights in a single forward pass.
A lightweight header-only C++ library for running Keras (TensorFlow) models without linking against TensorFlow.
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