Difference between revisions of "JavaScript"

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(A sampling of available Javascript Machine Learning libraries)
(Graphs Are Fundamental to Modern AI Systems | neo4j)
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* [[Knowledge Graphs]]
 
* [[Knowledge Graphs]]
 
* [http://d3js.org/ D3js.org] - [http://bl.ocks.org/heybignick/3faf257bbbbc7743bb72310d03b86ee8 D3.js v4 Force Directed Graph with Labels]
 
* [http://d3js.org/ D3js.org] - [http://bl.ocks.org/heybignick/3faf257bbbbc7743bb72310d03b86ee8 D3.js v4 Force Directed Graph with Labels]
 
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** [http://playground.tensorflow.org TensorFlow Deep Playground] an interactive visualization of neural networks, written in TypeScript using d3.js. Although this project basically contains a very basic playground for tensorflow, it can be repurposed for different means or used as a very impressive educational feature for different purposes.
 
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* [[Git - GitHub and GitLab]]
 
* [[Git - GitHub and GitLab]]

Revision as of 07:37, 7 April 2019

Youtube search... ...Google search

A sampling of available Javascript Machine Learning libraries

  • TensorFlow.js for training and deploying ML models in the browser and on Node.js (was called Deeplearnjs)
  • brain.js in browser and node.js ..see introduction
  • Neuro.js Learning to Drive project a deep learning and reinforcement learning Javascript library framework for the browser. Implementing a full stack neural-network based machine learning framework with extended reinforcement-learning support
  • TensorFire is a framework for running neural networks in the browser, accelerated by WebGL
  • Conventjs (not maintained)] neural networks supporting common modules, classification, regression, an experimental Reinforcement Learning module, able to train convolutional networks that process images
  • ConvNetJS an NPM version is also available for those using Node.js, and the library is designed to make proper use of JavaScript’s asynchronicity.
  • mxnet.js deep learning in browser (without server); allows you to mix symbolic and imperative programming on the fly with a graph optimization layer for performance
  • mljs includes supervised and unsupervised learning, artificial neural networks, regression algorithms and supporting libraries for statistics, math etc.
  • Synaptic multilayer perceptrons, multilayer long-short term memory networks, liquid state machines and a trainer capable of training a verity of networks
  • Webdnn this framework optimizes the DNN model to compress the model data and accelerate execution through JavaScript APIs such as WebAssembly and WebGPU
  • Neataptic neuro-evolution & backpropagation for the browser
  • Mind make predictions, using a matrix implementation to process training data and enabling configurable network topology.
  • DeeForge a development environment for deep learning

Graphs Are Fundamental to Modern AI Systems | neo4j

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Node.js

Youtube search...

Demos