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Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a multilayer perceptron model designed for pattern recognition and function approximation. The library includes a standalone inference engine that converts trained models into portable JavaScript functions. This allows predictions to be executed in browser or Node.js environments without requiring the original library dependencies. The system supports persistent model management through JSON serialization for saving and loading network weights. It also provides a streaming mechanism
Brain.js is a JavaScript neural network library for building, training, and running machine learning models in the browser or Node.js. It provides implementations for several network types, including feedforward networks, recurrent neural networks for time series forecasting, and autoencoders for data compression and denoising. The library features WebGL-based GPU acceleration to increase the speed of neural network computations on the graphics processor. It also includes a visualization tool that generates SVG images to represent the topology and layers of a feedforward network. The framewo
Synaptic is a JavaScript neural network library used for building, training, and executing neural networks in Node.js and the browser. It provides a framework for constructing architecture-free neural network topologies, a backpropagation training engine for weight optimization, and a toolkit for implementing recurrent neural network frameworks. The library enables the design of custom first or second order network architectures without predefined constraints. It supports a variety of specialized models, including Long Short-Term Memory networks, Hopfield networks, Liquid State Machines, and
OneFlow is a deep learning framework and distributed execution engine designed for building, training, and deploying neural network architectures. It functions as a scalable neural network library that allows for the development of deep learning models and their execution across distributed hardware. The project includes a machine learning graph compiler used to optimize neural network execution graphs. This allows for the acceleration of model performance and the reduction of latency during both training and inference. The framework covers broad capability areas including large-scale model
A JAX/Flax implementation of the RAFT optical flow estimator (https://arxiv.org/abs/2003.12039), ported from PyTorch (https://docs.pytorch.org/vision/main/models/raft.html). Checkpoints have been ported, too. The implementation has been tested to reproduce the original results.
The main features of alebeck/jax-raft are: Neural Network Libraries.
Projects with overlapping indexed features include: oneflow-inc/oneflow — OneFlow is a deep learning framework and distributed execution engine designed for building, training, and deploying… brainjs/brain.js — Brain.js is a JavaScript neural network library for building, training, and running machine learning models in the… cazala/synaptic — Synaptic is a JavaScript neural network library used for building, training, and executing neural networks in Node.js… harthur/brain — Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a… google-deepmind/penzai. erfanzar/easydel — EasyDeL is an open-source framework designed to enhance and streamline the training, fine-tuning, and serving of…