JavaScript

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• TensorFlow.js
• 10 Javascript IoT Libraries To Use In Your Next Project | Jonathan Saring
• Neural networks in JavaScript - Brain.js | Robert Plummer
• Visualization
• 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)
  • Keras.js runs Keras models in the browser, with GPU support using WebGL. since Keras uses a number of frameworks as backends, the models can be trained in TensorFlow, CNTK, and other frameworks as well.
  • 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
  • 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
  • brain.js several types of networks
  • 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
  • Compromise modest natural-language processing (NLP) interprets and pre-parses English and makes some reasonable decisions
  • Mind make predictions, using a matrix implementation to process training data and enabling configurable network topology.
  • Natural provides tokenizing, stemming (reducing a word to a not-necessarily morphological root), classification, phonetics, tf-idf, WordNet, string similarity, and more.
  • DeeForge a development environment for deep learning
• Graphs Are Fundamental to Modern AI Systems | neo4j
  • Why Knowledge Graphs Are Foundational to Artificial Intelligence | Jim Webber
  • Creating The Most Sophisticated Recommendations Using Native Graphs | Emil Eifrem
  • Neo4j Machine Learning Extensions
  • Review prediction with Neo4j and TensorFlow | David Mack
  • Knowledge Graphs
  • D3js.org - D3.js v4 Force Directed Graph with Labels

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• Git - GitHub and GitLab
• Bit

Node.js

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Demos

• Land Lines
• Learning to Drive
• TensorFlow.js Demos