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  "body_markdown": "Javascript is the \"Jack of all trades\" in programming languages. It can be praised just for its versatility. It powers the frontend, the back end, and even launch spacecraft in space. I wrote a post about famous Javascript libraries a few weeks ago which can be used for Machine Learning.\n10 famous Javascript libraries for Machine Learning\namananandrai ・ Dec 1 '20\n#javascript\n#machinelearning\n#datascience\n#beginners\nOne of the readers pointed out that this list includes libraries from large organizations. It was not my intention to bring a list of libraries backed by Google and such large companies and so I searched about some more libraries which can be used for Machine Learning. Some of these are suggested by the reader mentioned above. Here is a list of some more JS libraries for Machine Learning.\nSynaptic Js\nIt is a Javascript library for node.js and browsers. It can be used to make any type of ML architectures even second order neural networks that have higher-order weights. The core components of this library are neurons, layers, and networks along with trainers and architect.  The architect contains built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield networks.\nLink-\nhttp://caza.la/synaptic\nExamples\nDifferent type of Image Filters\nGrayscale\nNeon Glow\n* Painting an Image using Neural Network*\nStep 1\nStep 2\nNeataptic Js\n\"Neuro-evolution on steroids, right in the browser\"\nthis is what is written on the homepage of Neataptic. It is basically a JS library with neuroevolution as its building block. Neuroevolution means that evolutionary algorithms are used to train the neural network.\nInstinct\nalgorithm is used as the neuroevolution algorithm of this framework.\nNeataptic offers 6 pre-configured networks-\nGRU\nHopfield\nLSTM\nNARX\nPerceptron\nRandom\nLink-\nhttps://wagenaartje.github.io/neataptic/\nExamples\nAgar.io using Neuroevolution\nTarget seeking AI\nMind Js\nIt is a very light JS framework for ML which can be used to customize the network topology. It uses matrix implementation to train data. The tutorial of Mind can be found\nhere\n. A demo of\nmovie recommender system\nusing Mindjs can be found\nhere\n.\nLink-\nhttps://github.com/stevenmiller888/mind\nLiquid Carrot\nIt is another Javascript framework based on neuroevolution. It is inspired by NeatapticJs for neuroevolution for neural training and SynapticJs for architecture free neural networks. It can be used for both node.js and browser. A demo for Flappy bird can be seen\nhere\n. It has pre-configured architectures for GRU, LSTM, NARX, and Hopfield Network. Multithreading and GPU support for training is coming soon.\nLink-\nhttps://liquidcarrot.io/\nExample\nFlappy Bird using Population based Evolutionary Algorithm\nMl js\nThis library is a compilation of the tools developed in the mljs organization. It is mainly maintained for use in the browser.\nTo include the ml.js library in a web page:\n<script src=\"https://www.lactame.com/lib/ml/4.0.0/ml.min.js\"></script>\nIt has tools for a lot of Machine Learning algorithms. For unsupervised learning it has\nPrincipal component analysis (PCA)\nHierarchical clustering\nK-means clustering\nIt has many supervised learning algorithms like-\nNaive Bayes\nK-Nearest Neighbor (KNN)\nDecision tree classifier\nRandom forest classifier\nIt is a gem of a library for Regression with varied type of regression algorithms.\nSimple linear regression\nPolynomial regression\nMultivariate linear regression\nPower regression\nExponential regression\nTheil-Sen regression\nRobust polynomial regression\nDecision tree regression\nRandom forest regression\nLink-\nhttps://github.com/mljs/ml\nConvnet Js\nConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat.\nThe description above on the homepage of ConvnetJs aptly describes this framework. This library is developed by a Ph.D. student at Stanford University. It was originally having support for Convolutional Neural Networks, but now it supports Common Neural Networks, Classification using SVM, L2 regularisation, and also Reinforcement Learning.\nA very good visualization of CNN on CIFAR-10 dataset can be found\nhere\n. It is done using JS and on your browser. It will help you better understand CNN.\nLink-\nhttps://cs.stanford.edu/people/karpathy/convnetjs/\nExample\nMNIST dataset digit recognition\nHope you all liked this article and will try some of these libraries for training your own neural networks.",
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