The extreme growth in new technologies in the field of machine learning has helped software developers build new AI applications in ways easier than ever.
Tensorflow.js can be used directly in the browsers while leveraging WebGL for accelerations. The Tensorflow.js model of supporting both browsers and Node.js environments has been adopted by many open-source libraries including brain.js and machinelearn.js.
stidlib is the fastest, easiest way to build infinitely scalable, self-healing APIs. Standard Library is based on Function as a Service (“serverless”) architecture, initially popularized by AWS Lambda. You can use Standard Library to build modular, scalable APIs for yourself and other developers in minutes without having to manage servers, gateways, domains, write documentation, or build SDKs. Your development workflow has never been easier – focus on writing code you love, let Standard Library handle everything else.
ConvNetJS is another library for neural networks and deep learning. It enables training neural networks in browsers. In addition to classification and regression problems, it has the reinforcement learning module (using Q-learning) that is still experimental. ConvNetJS provides support for convolutional neural networks that excel in image recognition.
Neataptic offers flexible neural networks; neurons and synapses can be removed with a single line of code. No fixed architecture is required for neural networks to function at all. This flexibility allows networks to be shaped for your dataset through neuro-evolution, which is done using multiple threads.
Synaptic implements a general “architecture free” algorithm that can be used to create a wider range of network types than usually encountered. It comes with some predefined networks – multilayer perceptrons, multilayer long-short term memory networks, liquid state machines, and so on.
Keras has become the leading neural network library for the creation and preparing of profound learning models over a huge scope of platforms. Written in Python and boasting more than 250,000 individual clients, it is the second most prominent deep learning structure after TensorFlow.
Limdu.js is a machine learning framework for Node.js that supports Binary classification, multi-label classification, feature engineering, online learning, and real-time classification. It is currently in alpha state and looking for contributors.
MXNetJS allows you to run the prediction of state-of-art deep learning models in any computational graph and brings the fun of deep learning to the client-side.
Synapses is a lightweight Neural Network library, for js, JVM and .net. It features
fitNetwork which is a new neural network trained with a single observation.