Difference between revisions of "JavaScript"

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(Graphs Are Fundamental to Modern AI Systems | neo4j)
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* [http://github.com/dmlc/mxnet.js/ 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
 
* [http://github.com/dmlc/mxnet.js/ 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
  
==== [http://neo4j.com/use-cases/artificial-intelligence-analytics/ Graphs Are Fundamental to Modern AI Systems | neo4j] ====
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==== Graphics ====
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* [http://neo4j.com/use-cases/artificial-intelligence-analytics/ Graphs Are Fundamental to Modern AI Systems | neo4j]
 
* [http://www.datanami.com/2018/03/20/why-knowledge-graphs-are-foundational-to-artificial-intelligence/ Why Knowledge Graphs Are Foundational to Artificial Intelligence | Jim Webber]
 
* [http://www.datanami.com/2018/03/20/why-knowledge-graphs-are-foundational-to-artificial-intelligence/ Why Knowledge Graphs Are Foundational to Artificial Intelligence | Jim Webber]
 
* [http://www.cbronline.com/opinion/creating-sophisticated-recommendations-native-graphs Creating The Most Sophisticated Recommendations Using Native Graphs | Emil Eifrem]
 
* [http://www.cbronline.com/opinion/creating-sophisticated-recommendations-native-graphs Creating The Most Sophisticated Recommendations Using Native Graphs | Emil Eifrem]
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* [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]
 
** [http://playground.tensorflow.org TensorFlow Deep Playground] an interactive visualization of neural networks, written in TypeScript using d3.js and can be repurposed for different means  
 
** [http://playground.tensorflow.org TensorFlow Deep Playground] an interactive visualization of neural networks, written in TypeScript using d3.js and can be repurposed for different means  
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* [http://get.webgl.org/ WebGL.js] - (Web Graphics Library) is a JavaScript API for rendering interactive 3D and 2D graphics within any compatible web browser without the use of plug-ins.
 
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* [[Git - GitHub and GitLab]]
 
* [[Git - GitHub and GitLab]]

Revision as of 05:32, 25 July 2019

Youtube search... ...Google search

A sampling of available Javascript Machine Learning libraries

  • Libraries for NLP see Natural Language Tools & Services
  • TensorFlow.js for training and deploying ML models in the browser and on Node.js (was called Deeplearnjs)
    • Keras.js No longer active - capability now is in TensorFlow.js
    • Deeplearnjs rebranded to TensorFlow.js
  • Tensorflow Deep Playground; written in Typescript using d3.js
  • brain.js in browser and Node.js ..see introduction
  • Natural provides tokenizing, stemming (reducing a word to a not-necessarily morphological root), classification, phonetics, tf-idf, WordNet, string similarity, some inflections, and more. (nodejs)
  • 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.
  • 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
  • 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

Graphics

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1*W79wtpbE2NTqUuFA8EVorQ.png

Node.js

Youtube search...

Demos