Neural Networks
Showing 36 of 428 projects
A hands-on workshop introducing deep learning concepts with practical examples using neural networks, CNNs, RNNs, and autoencoders.
A production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
Flax implementations and pretrained checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX.
A Julia library providing a consistent API for common machine learning algorithms, designed for practitioners working with in-memory datasets.
A machine learning and optimization framework for Objective-C and Swift, focused on regression and multi-objective evolutionary algorithms.
A deep belief net and deep learning implementation written in F# with GPU acceleration via Alea.cuBase.
A deep learning model for classifying image aesthetic quality using Inception modules and fine-tuned connected layers.
A Haskell library for building and training feed-forward neural networks with automatic differentiation.
A GPU-accelerated (CUDA) C++ template library for building and training artificial neural networks, including self-organizing maps and back-propagation networks.
A lightweight Clojure wrapper for TensorFlow's Java API, providing idiomatic access to machine learning operations.
A Swift library for accelerated tensor operations and dynamic neural networks with automatic differentiation, supporting all Apple platforms and Linux.
A small Clojure library for constructing and training neural networks using core.matrix.
A flexible deep learning framework for Ruby, ported from Python's Chainer.
A Clojure wrapper for Deeplearning4j, providing idiomatic access to neural networks, data import, and distributed training.
A Keras implementation of Ladder Networks for semi-supervised learning, achieving 98% accuracy on MNIST with only 100 labeled examples.
An experimental extension to Torch7's nn package, providing unproven neural network modules and optimizations.
An all-in-one tool for creating, training, debugging, and sharing HTM neural networks using the original NuPIC library.
A collection of Jupyter notebooks for learning the neon deep learning framework through hands-on tutorials.
A lightweight, platform-independent tensor library with autograd for the JVM, accelerated by OpenCL.
A deep learning system for automatic spoken language identification from audio files using TensorFlow and Caffe.
A Delphi/Pascal binding for TensorFlow and Keras that enables Pascal developers to build, train, and deploy machine learning models.
A Clojure library for building and training neural networks with support for various architectures and learning algorithms.
Crystal language bindings for the FANN (Fast Artificial Neural Network) C library.
A neural network-based password cracking tool using character-level RNNs to learn and generate password guesses.
TensorFlow implementation of hierarchical attention networks for document classification using GRU cells and attention mechanisms.
A GUI-based tool for training deep neural networks to segment biological images using corrective annotation.
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A pre-release Caffe branch for fully convolutional networks (FCNs), now deprecated with features merged into Caffe master.
A TensorFlow implementation of Attend, Infer, Repeat (AIR), a generative model for fast scene understanding by reconstructing objects sequentially.
A learning-focused, high-performance tensor computation library built from scratch in Rust with automatic differentiation and CPU/CUDA backends.
A neural network model that integrates neighbor information from heterogeneous networks to discover new drug-target interactions.
A modern, fast, and modular deep learning and machine learning framework for Python built on PyTorch.
A collection of neural network libraries for functional and mainstream languages, offering efficient lazy evaluation and cross-language compatibility.
A PyTorch-based Python library for energy-based machine learning models, including Restricted Boltzmann Machines and Deep Belief Networks.
A Go implementation of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm for evolving neural network structures.
A PyTorch-based tool for training custom DeepDream models using GoogleNet and custom image datasets.
A Common Lisp implementation for Llama inference operations, enabling LLM experimentation and integration with symbolic AI systems.
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