Neural Networks
Showing 36 of 159 projects
A curated list of resources dedicated to recurrent neural networks (RNNs) and deep learning.
A high-performance deep learning library written entirely in Swift, optimized for Apple hardware.
A collection of simple tutorials introducing deep learning concepts using Google's TensorFlow framework.
A header-only, dependency-free deep learning framework in C++14 for embedded systems and IoT devices.
A flexible Python deep learning framework using define-by-run dynamic computational graphs for neural network research.
A library for building and evaluating mathematical expressions and neural networks in Go, with automatic differentiation and GPU support.
A collection of TensorFlow tutorials covering basics to advanced neural network architectures with Python code and notebooks.
An open-source Go engine that replicates AlphaGo Zero's architecture, learning solely through self-play without human knowledge.
A Python library implementing state-of-the-art deep reinforcement learning algorithms with seamless Keras integration.
An open-source machine learning framework for building classical, deep, or hybrid ML applications with a focus on performance and portability.
Rust language bindings for TensorFlow, providing idiomatic access to machine learning capabilities.
A fast, flexible C++ standalone library for machine learning with high-performance defaults and total internal modifiability.
A fast, flexible C++ standalone library for machine learning with high-performance defaults and total internal modifiability.
DeepMind's library for building graph networks in TensorFlow and Sonnet, enabling graph-structured data processing with neural networks.
Rust bindings for the C++ API of PyTorch, providing thin wrappers around libtorch.
A Python library for image augmentation in machine learning, offering a stochastic pipeline approach with fine-grained control over operations.
Code repository for the 'Machine Learning with PyTorch and Scikit-Learn' book, providing practical examples and notebooks.
A hands-on beginner's guide to machine learning and image classification using Caffe and DIGITS with neural networks.
A PyTorch library providing GPU-accelerated tools for 3D deep learning, including differentiable rendering and geometric operations.
A curated collection of links to conference publications, surveys, and software in graph-based deep learning.
Run trained Keras models directly in the browser with GPU acceleration via WebGL.
A Python module for easily training character- or word-level text-generating neural networks on any dataset with minimal code.
An engine-agnostic deep learning framework for Java developers, providing a high-level API for model training and inference.
A high-level library for training and evaluating neural networks in PyTorch with a flexible engine and event system.
A PyTorch system for open-domain question answering by retrieving and reading documents, originally applied to Wikipedia.
Official code repository for the 'Machine Learning with TensorFlow' book with practical examples.
A library for probabilistic reasoning and statistical analysis integrated with TensorFlow and JAX.
An open-source library for training and deploying deep learning recommendation models with sparse data at scale using multi-GPU support.
A fast parallel implementation of the Connectionist Temporal Classification (CTC) loss function for CPU and GPU.
A Python library offering scalable and user-friendly implementations of state-of-the-art neural forecasting models.
Intel's reference deep learning framework designed for high performance across CPUs, GPUs, and custom hardware.
A MATLAB/Octave toolbox for deep learning with implementations of neural networks, deep belief nets, autoencoders, and convolutional networks.
A collection of handwritten notes, notebooks, and resources for Andrew Ng's Deep Learning Specialization on Coursera.
Automatic and interactive image colorization using deep neural networks, with PyTorch models for ECCV 2016 and SIGGRAPH 2017 papers.
A neural network library optimized for dynamic structures that change per training instance, with C++ and Python bindings.
PyTorch implementation of FlowNet 2.0 for optical flow estimation using deep neural networks.
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