Book Image

Hands-on Machine Learning with JavaScript

Book Image

Hands-on Machine Learning with JavaScript

Overview of this book

In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications.
Table of Contents (14 chapters)

Summary

In this chapter, we discussed two advanced neural network topologies: the CNN and the RNN. We discussed the CNN in the context of image recognition, specifically the problem of handwritten digit identification. While exploring the CNN, we also discussed the convolution operation itself in the context of image filtering.

We also discussed how neural networks can be made to retain memory through the RNN architecture. We learned that RNNs have many applications, ranging from time-series analysis to natural language modeling. We discussed several RNN architecture types, such as the simple fully recurrent network and the GRU network. Finally, we discussed the state-of-the-art LSTM topology, and how it can be used for language modeling and other advanced problems, such as image captioning or video annotation.

In the next chapter, we'll take a look at some practical approaches...