JavaScript, one of the core programming languages for the web-predicated application, has been utilized by the researchers to implement and develop deep learning techniques. For the developers who have experience in JavaScript and deep learning techniques, the following libraries will be auxiliary to design deep learning demos.
In this article, we list down eight JavaScript libraries that are designed for deep learning development.
1 | Brain.js
Indited in JavaScript, Brain.js is a GPU expedited library for the development of Neural Network models. This library is simple, expeditious, facile-to-use and can be utilized with Node.js or in the browser. The library withal performs computations with or without utilizing GPU and provides multiple neural network implementations.
2 | ConvNetJS
ConvNetJS is a popular Javascript library for training deep learning models (Neural Networks) entirely in the browser. Indited by a researcher at Stanford University, this library sanctions to formulate and solve Neural Networks in Javascript and has the following features.
- It includes Common Neural Network modules that contain fully connected layers and non-linearities.
- It supports Classification (SVM/Softmax) and Regression (L2) cost functions
- ConvNetJS has the ability to specify and train Convolutional Networks that process images
- It also supports an experimental Reinforcement Learning module which is based on Deep Q Learning.
3 | Deeplearn.js
Deeplearn.js is an open-source hardware-expedited JavaScript library for the development of deep learning models. Pristinely developed by the Google Encephalon PAIR team, this library avails to build intuitive deep learning implements for the browser. It sanctions a researcher to train neural networks in a browser or run pre-trained models in the inference mode.
4 | Mind
Mind is a flexible neural network library for Node.js and the browser which is inscribed in JavaScript. This library utilizes a matrix implementation to process training data and sanctions you to customize the network topology. It is pluggable in nature which denotes one can facilely download or upload the plugins provided to configure pre-trained networks that can be facilely used to make prognostications.
5 | Neuro.js
Neuro.js is a library for developing and training deep learning models in JavaScript and can be deployed in the browser or Node.js. This library fortifies Multi-label relegation, online learning as well as genuine-time relegation and can be habituated to build AI auxiliaries and chatbots.
6 | Synaptic
Synaptic is a JavaScript Library for developing neural network models in the browser or in Node.js. Its generalized algorithm is architecture-free, so one can facilely build and train fundamentally any type of first-order or even second-order neural network architectures. The library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term recollection networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable of training any given network, which includes built-in training tasks/tests like solving an XOR, consummating a Diverted Sequence Recall task or an Embedded Reber Grammar test. This avails in testing as well as comparing the performance of different neural net architectures.
7 | TensorFlow.js
TensorFlow.js is an open-source hardware-expedited library inscribed in Javascript for the development of machine learning and deep learning models. The TensorFlow.js data provides simple APIs to load and parse data from disk or over the web in a variety of formats, and to prepare that data for use in machine learning models. With this library, one can utilize flexible and intuitive APIs to build models from scratch utilizing the low-level JavaScript linear algebra library or the high-level layers API.
8 | WebDNN
WebDNN is a JavaScript library that is built to run deep neural network pre-trained models on the browser. This library provides DNN applications to culminate-users by utilizing the web browser as an installation-free DNN execution framework. It optimizes a trained DNN model to compress the model data and expedite the execution and executes it with JavaScript API such as WebAssembly and WebGPU.
