Difference between revisions of "JavaScript"
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* [http://blog.bitsrc.io/10-javascript-iot-libraries-to-use-in-your-next-projects-bef5f9136f83 10 Javascript IoT Libraries To Use In Your Next Project |] [http://blog.bitsrc.io/@JonathanSaring Jonathan Saring] | * [http://blog.bitsrc.io/10-javascript-iot-libraries-to-use-in-your-next-projects-bef5f9136f83 10 Javascript IoT Libraries To Use In Your Next Project |] [http://blog.bitsrc.io/@JonathanSaring Jonathan Saring] | ||
* A [http://news.ycombinator.com/item?id=16718285 sampling] of [http://blog.bitsrc.io/11-javascript-machine-learning-libraries-to-use-in-your-app-c49772cca46c available] Javascript Machine Learning libraries: | * A [http://news.ycombinator.com/item?id=16718285 sampling] of [http://blog.bitsrc.io/11-javascript-machine-learning-libraries-to-use-in-your-app-c49772cca46c available] Javascript Machine Learning libraries: | ||
| − | * [http://transcranial.github.io/keras-js/#/ 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. | + | ** [http://transcranial.github.io/keras-js/#/ 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. |
| − | * [http://github.com/NeuroJS Neuro.js] [http://janhuenermann.com/projects/learning-to-drive 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 | + | ** [http://github.com/NeuroJS Neuro.js] [http://janhuenermann.com/projects/learning-to-drive 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 |
| − | * [http://github.com/karpathy/convnetjs Conventjs] (not maintained)] neural networks supporting common modules, classification, regression, an experimental Reinforcement Learning module, able to train convolutional networks that process images | + | ** [http://github.com/karpathy/convnetjs Conventjs] (not maintained)] neural networks supporting common modules, classification, regression, an experimental Reinforcement Learning module, able to train convolutional networks that process images |
| − | * [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://github.com/BrainJS/brain.js brain.js] several types of networks | + | ** [http://github.com/BrainJS/brain.js brain.js] several types of networks |
| − | * [http://github.com/mljs/ml mljs] includes supervised and unsupervised learning, artificial neural networks, regression algorithms and supporting libraries for statistics, math etc. | + | ** [http://github.com/mljs/ml mljs] includes supervised and unsupervised learning, artificial neural networks, regression algorithms and supporting libraries for statistics, math etc. |
| − | * [http://github.com/cazala/synaptic Synaptic] multilayer perceptrons, multilayer long-short term memory networks, liquid state machines and a trainer capable of training a verity of networks | + | ** [http://github.com/cazala/synaptic Synaptic] multilayer perceptrons, multilayer long-short term memory networks, liquid state machines and a trainer capable of training a verity of networks |
| − | * [http://mil-tokyo.github.io/webdnn/ Webdnn] this framework optimizes the DNN model to compress the model data and accelerate execution through JavaScript APIs such as WebAssembly and WebGPU | + | ** [http://mil-tokyo.github.io/webdnn/ Webdnn] this framework optimizes the DNN model to compress the model data and accelerate execution through JavaScript APIs such as WebAssembly and WebGPU |
| − | * [http://wagenaartje.github.io/neataptic/ Neataptic] neuro-evolution & backpropagation for the browser | + | ** [http://wagenaartje.github.io/neataptic/ Neataptic] neuro-evolution & backpropagation for the browser |
| − | * [http://compromise.cool/ Compromise] modest natural-language processing (NLP) interprets and pre-parses English and makes some reasonable decisions | + | ** [http://compromise.cool/ Compromise] modest natural-language processing (NLP) interprets and pre-parses English and makes some reasonable decisions |
| − | * [http://stevenmiller888.github.io/mindjs.net/ Mind] make predictions, using a matrix implementation to process training data and enabling configurable network topology. | + | ** [http://stevenmiller888.github.io/mindjs.net/ Mind] make predictions, using a matrix implementation to process training data and enabling configurable network topology. |
| − | * [http://github.com/NaturalNode/natural Natural] provides tokenizing, stemming (reducing a word to a not-necessarily morphological root), classification, phonetics, tf-idf, WordNet, string similarity, and more. | + | ** [http://github.com/NaturalNode/natural Natural] provides tokenizing, stemming (reducing a word to a not-necessarily morphological root), classification, phonetics, tf-idf, WordNet, string similarity, and more. |
| − | * [http://deepforge.readthedocs.io/en/latest/deployment/overview.html DeeForge] a development environment for deep learning | + | ** [http://deepforge.readthedocs.io/en/latest/deployment/overview.html DeeForge] a development environment for deep learning |
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Revision as of 16:13, 5 August 2018
- TensorFlow.js for training and deploying ML models in the browser and on Node.js (was called Deeplearnjs)
- TensorFlow Playground written in Javascript (TypeScript) using d3.js
- 10 Javascript IoT Libraries To Use In Your Next Project | Jonathan Saring
- A sampling of available Javascript Machine Learning libraries:
- 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
- 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
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Node.js
