Book Image

Hands-On Machine Learning with TensorFlow.js

By : Kai Sasaki
Book Image

Hands-On Machine Learning with TensorFlow.js

By: Kai Sasaki

Overview of this book

TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge. By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
5
Section 2: Real-World Applications of TensorFlow.js
12
Section 3: Productionizing Machine Learning Applications with TensorFlow.js

Unsupervised Learning

So far, we've demonstrated how supervised learning works by looking at examples of regression and classification problems. In supervised learning, we already know the answer that will be predicted. In this chapter, the unsupervised learning problem will be introduced. This type of problem doesn't need the dataset to include the target value. We need to find the hidden pattern without any explicit target.

The clustering problem is a typical setting for unsupervised learning. It tries to make a group of samples in a natural manner. This chapter covers some ideas and algorithms that are useful for making groups of data points that focus on the implementation of the K-means algorithm.

The following topics will be covered in this chapter:

  • What is unsupervised learning?
  • Learning how K-means works
  • Generalizing K-means with the EM algorithm
  • Clustering...