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Keras-js is a JavaScript inference engine and browser-based machine learning framework designed to execute pre-trained Keras neural networks. It allows for client-side model inference in web browsers or Node.js environments without the requirement of a backend server.
The main features of transcranial/keras-js are: Keras Model Execution, Web-Based Machine Learning, Client-Side Inference, Browser-Based Frameworks, GPU-Accelerated Computation, WebGL Tensor Acceleration, Inference Execution, External Model Loading.
Projects with overlapping indexed features include: tensorflow/tfjs — TensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and… harthur/brain — Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a… lasagne/lasagne — Lasagne is a modular neural network framework and symbolic computation engine used for building and training deep… graal-research/poutyne — A simplified framework and utilities for PyTorch. cornellius-gp/gpytorch — GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process… deepmind/sonnet — Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep…
TensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and server-side environments. It functions as a browser-based model trainer, a WebAssembly inference engine, and a WebGPU accelerated tensor library for low-level linear algebra. The project also includes a model converter to transform Python-based models into optimized formats for JavaScript execution. The library distinguishes itself through a pluggable backend architecture that allows mathematical operations to be executed via CPU, WebGL, or WebGPU. It supports the conversion of Py
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
Lasagne is a modular neural network framework and symbolic computation engine used for building and training deep learning architectures. Built as a library on top of Theano, it utilizes symbolic expression graphs and lazy evaluation to automate gradient calculations for parameter optimization. The framework emphasizes modularity by allowing the construction of complex neural networks through the composition of independent and reusable layers. It is designed as a hardware-accelerated machine learning library that offloads intensive linear algebra operations to graphics processors to increase
Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states. The project provides specialized tools for distributed model training, enabling the synchronization of gradients across multiple hardware devices. It also serves as a model state management system, allowing for the persistence of neural network weights and the export of portable models that separate the computation g