McCaffrey J. Neural Networks with JavaScript Succinctly

Morrisville (NC): Syncfusion, 2019. — 162 p.This e-book assumes you have intermediate or better programming skills, but does not assume you know anything about neural networks. This e-book presents complete example programs for the three major types of neural network problems. A multiclass classification problem predicts a discrete value where there are three or more possible values, for example, predicting the size of company (small, medium, large). A regression problem predicts a single numeric value, for example, predicting a student's GPA. A binary classification problem predicts a discrete value where there are only two possible values, for example, predicting the sex of a student. Everyone I know learns a new programming technology in the same way: you get an example program up and running and then experiment with the code. All the program code is presented in this e-book so you can copy and paste it, and run the demo programs. The data files used by the programs are in the appendix of this e-book. You can find the program code and data on GitHub. To prepare, all you have to do is install Node.js and use the code and data presented here. No other software is needed.
All the neural network examples in this e-book use a single hidden layer of nodes. Neural networks that have two or more hidden layers are called deep neural networks. In theory (the Universal aproximation Theorem, sometimes called the Cybenko Theorem), a neural network with a single hidden layer can compute anything that a neural network with multiple hidden layers can. But in practice, deep neural networks can solve some problems that simple neural networks cannot. Other forms of deep neural networks use complex architectures. Examples include convolutional neural networks (often used to classify images) and recurrent neural networks (often used for natural language processing). Implementing a neural network from scratch gives you complete control over your system and enables you to have a complete understanding of your system. The techniques used in this e-book can be extended to neural networks with two hidden layers. But for three or more hidden layers, or for deep neural networks with complex architectures, implementing from scratch is not practical in most situations. For these scenarios, you should use a deep neural network code library, such as TensorFlow, Keras, or PyTorch. However, at my company (Microsoft), where we have hundreds of engineers working on deep neural systems, it's considered essential to have a solid foundation that can be best gained by understanding single, hidden-layer neural networks and implementing them from scratch. Much of the content presented in this e-book was developed for engineers who are new to the field of machine learning and neural networks.Getting Started.
Input and Output.
Training.
Overfitting.
Regression.
Binary Classification.
Appendix — Data Files.