Top 6 Machine Learning Libraries for JavaScript in 2019


Top 6 Machine Learning Libraries for JavaScript in 2019

Explore the top six machine learning libraries for JavaScript in 2019.

Usually, people apply machine learning (ML) methods and algorithms using one of two programming languages: Python or R. Books, courses, and tutorials about machine learning most often use one of these languages as well (or both).

Python is a general-purpose programming language used not only for machine learning but also for scientific computing, back-end web development, desktop applications, etc. R is created primarily for statisticians. However, they have at least two common characteristics:

  • They are suitable for non-programmers
  • They have comprehensive ML libraries

In many cases, ML algorithms are implemented in Fortran, C, C++, or Cython and called from Python or R.

Java is also used for Machine Learning, but usually by professional programmers.

During the last few years, JavaScript gained popularity, and some very interesting machine learning libraries appeared, enabling the implementation of ML methods in browsers or on Node.js. Surprisingly, many of these libraries implement a lot of code in JavaScript.

This article presents several ML open-source libraries for JavaScript:

  • ml.js
  • TensorFlow.js
  • brain.js
  • ConvNetJS
  • WebDNN
  • natural
ml.js

ml.js is a comprehensive, general-purpose JavaScript ML library for browsers and Node.js. It offers the routines for:

  • Bit operations on arrays, hash tables, sorting, random number generation, etc.
  • Linear algebra, array manipulation, optimization (the Levenberg-Marquardt method), statistics
  • Cross-validation
  • Supervised learning
  • Unsupervised learning

Supported supervised learning methods are:

  • Linear, polynomial, exponential, and power regression
  • K-nearest neighbors
  • Naive Bayes
  • Support vector machines
  • Decision trees and random forest
  • Feedforward neural networks, etc.

Besides, ml.js offers several unsupervised learning methods:

  • Principal component analysis
  • Cluster analysis (k-means and hierarchical clustering)
  • Self-organizing maps (Kohonen networks)

License: MIT.

TensorFlow.js

TensorFlow is one of the most popular Machine Learning libraries. It focuses on various types and structures of artificial neural networks, including deep networks as well as the components of the networks.

TensorFlow is created by Google Brain Team and written in C++ and Python. However, it can be used with several languages including JavaScript.

TensorFlow is a very comprehensive library that still enables building and training models easily. It supports a huge variety of network layers, activation functions, optimizers, and other components. It has good performance and offers GPU support.

TensorFlow.js is a JavaScript ML library for use in browsers or on Node.js. It supports WebGL.

License: Apache 2.0.

brain.js

brain.js is a library written in JavaScript — focused on training and applying feedforward and recurrent neural networks. It also offers additional utilities, such as math routines necessary for neural networks.

It provides advanced options like:

  • Using GPU to train networks
  • Asynchronous training that can fit multiple networks in parallel
  • Cross-validation that is a more sophisticated validation method

brain.js saves and loads models to/from JSON files.

License: MIT.

ConvNetJS

ConvNetJS is another library for neural networks and deep learning. It enables training neural networks in browsers. In addition to classification and regression problems, it has the reinforcement learning module (using Q-learning) that is still experimental. ConvNetJS provides support for convolutional neural networks that excel in image recognition.

In ConvNetJS, neural networks are lists of layers. It provides the following layers:

  • Input (the first) layer
  • Fully connected layer
  • Convolution layer
  • Pooling layer
  • Local contrast normalization layer
  • Classifiers loss (the output) layers: softmax and svm
  • Regression loss (the output) layer that uses L2

It supports several important activation functions like:

  • ReLU
  • Sigmoid
  • Hyperbolic tangent
  • MaxOut

as well as the optimizers such as:

  • Stochastic gradient descent
  • Adadelta
  • AdagradS
  • ConvNetJS also provides a convenient way to save and load models to/from JSON files.

License: MIT.

WebDNN

WebDNN is a library focused on deep neural networks, including recurrent neural networks with LSTM architecture. It is written in TypeScript and Python and offers JavaScript and Python APIs.

It also provides the possibility of GPU execution in browsers.

A very convenient feature of WebDNN is the possibility to convert and use the models pre-trained with PyTorch, TensorFlow, Keras, Caffemodel, or Chainer.

License: MIT.

natural

natural is a JavaScript library for natural language processing used with Node.js.

It supports:

  • Tokenization (breaking text into arrays of strings)
  • Calculation of strings distances
  • Matching similar strings
  • Classification (naive Bayes, logistic regression, and maximum entropy)
  • Sentiment analysis (currently in eight languages)
  • Phonetic matching, inflectors, n-grams, etc.

License: MIT.

Conclusion

Both JavaScript and machine learning have gained much attention and popularity during the last several years. Although initially created to enable dynamic behavior of web pages, JavaScript becomes one of the languages of choice to implement and apply machine learning methods, especially in browsers or servers (Node.js).

This article provided the initial information on the availability of machine learning libraries for JavaScript.

Have a lot of fun exploring them and thank you for reading!

Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.

Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03

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Machine Learning in JavaScript with TensorFlow.js

Machine Learning in JavaScript with TensorFlow.js

TensorFlow.js is a library for Machine Learning in JavaScript. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. Interested in using Machine Learning in JavaScript applications and websites? If you’re a Javascript developer who’s new to ML, TensorFlow.js is a great way to begin learning. Or, if you’re a ML developer who’s new to Javascript, read on to learn more about new opportunities for in-browser ML.

We’re excited to introduce TensorFlow.js, an open-source library you can use to define, train, and run machine learning models entirely in the browser, using Javascript and a high-level layers API. If you’re a Javascript developer who’s new to ML, TensorFlow.js is a great way to begin learning. Or, if you’re a ML developer who’s new to Javascript, read on to learn more about new opportunities for in-browser ML. In this post, we’ll give you a quick overview of TensorFlow.js, and getting started resources you can use to try it out.

In-Browser ML

Running machine learning programs entirely client-side in the browser unlocks new opportunities, like interactive ML! If you’re watching the livestream for the TensorFlow Developer Summit, during the TensorFlow.js talk you’ll find a demo where @dsmilkov and @nsthorat train a model to control a PAC-MAN game using computer vision and a webcam, entirely in the browser. You can try it out yourself, too, with the link below — and find the source in the examples folder.

If you’d like to try another game, give the Emoji Scavenger Hunt a whirl — this time, from a browser on your mobile phone.

ML running in the browser means that from a user’s perspective, there’s no need to install any libraries or drivers. Just open a webpage, and your program is ready to run. In addition, it’s ready to run with GPU acceleration. TensorFlow.js automatically supports WebGL, and will accelerate your code behind the scenes when a GPU is available. Users may also open your webpage from a mobile device, in which case your model can take advantage of sensor data, say from a gyroscope or accelerometer. Finally, all data stays on the client, making TensorFlow.js useful for low-latency inference, as well as for privacy preserving applications.

What can you do with TensorFlow.js?

If you’re developing with TensorFlow.js, here are three workflows you can consider.

  • You can import an existing, pre-trained model for inference. If you have an existing TensorFlow or Keras model you’ve previously trained offline, you can convert into TensorFlow.js format, and load it into the browser for inference.
  • You can re-train an imported model. As in the Pac-Man demo above, you can use transfer learning to augment an existing model trained offline using a small amount of data collected in the browser using a technique called Image Retraining. This is one way to train an accurate model quickly, using only a small amount of data.
  • Author models directly in browser. You can also use TensorFlow.js to define, train, and run models entirely in the browser using Javascript and a high-level layers API. If you’re familiar with Keras, the high-level layers API should feel familiar.
Let’s see some code

If you like, you can head directly to the samples or tutorials to get started. These show how-to export a model defined in Python for inference in the browser, as well as how to define and train models entirely in Javascript. As a quick preview, here’s a snippet of code that defines a neural network to classify flowers, much like on the getting started guide on TensorFlow.org. Here, we’ll define a model using a stack of layers.

import * as tf from ‘@tensorflow/tfjs’;
const model = tf.sequential();
model.add(tf.layers.dense({inputShape: [4], units: 100}));
model.add(tf.layers.dense({units: 4}));
model.compile({loss: ‘categoricalCrossentropy’, optimizer: ‘sgd’});

The layers API we’re using here supports all of the Keras layers found in the examples directory (including Dense, CNN, LSTM, and so on). We can then train our model using the same Keras-compatible API with a method call:

await model.fit(
  xData, yData, {
    batchSize: batchSize,
    epochs: epochs
});

The model is now ready to use to make predictions:

// Get measurements for a new flower to generate a prediction
// The first argument is the data, and the second is the shape.
const inputData = tf.tensor2d([[4.8, 3.0, 1.4, 0.1]], [1, 4]);

// Get the highest confidence prediction from our model
const result = model.predict(inputData);
const winner = irisClasses[result.argMax().dataSync()[0]];

// Display the winner
console.log(winner);

TensorFlow.js also includes a low-level API (previously deeplearn.js) and support for Eager execution. You can learn more about these by watching the talk at the TensorFlow Developer Summit.


An overview of TensorFlow.js APIs. TensorFlow.js is powered by WebGL and provides a high-level layers API for defining models, and a low-level API for linear algebra and automatic differentiation. TensorFlow.js supports importing TensorFlow SavedModels and Keras models.

How does TensorFlow.js relate to deeplearn.js?

Good question! TensorFlow.js, an ecosystem of JavaScript tools for machine learning, is the successor to deeplearn.js which is now called TensorFlow.js Core. TensorFlow.js also includes a Layers API, which is a higher level library for building machine learning models that uses Core, as well as tools for automatically porting TensorFlow SavedModels and Keras hdf5 models. For answers to more questions like this, check out the FAQ.