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

Exercise

  1. Come up with a situation around you that can be defined as an MDP problem.
  2. Do you think we can use the state-value function to solve the MDP problem in the same way as we use the action-value function?
  3. Explore the active-function result by changing the following hyperparameters for the four-states MDP introduced here:
    1. Discount ratio
    2. Learning rate
    3. Reward in the transition from state 2 to 3
  1. Use the following policy for the four-states MDP introduced in the chapter:
    1. Always choose action 1.
    2. Always choose action 2.
    3. Choosing the action maximizing the action value.
  2. Try to run the CartPole example in the example code and see how the behavior is changed.