Hands-On Machine Learning with TensorFlow.js
- Build, train and run machine learning models in the browser using TensorFlow.js
- Create smart web applications from scratch with the help of useful examples
- Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function
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.
What you will learn
- Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset
- Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js
- Apply the Bellman equation to solve MDP problems
- Use the k-means algorithm in TensorFlow.js to visualize prediction results
- Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps
- Implement tf.js backend frameworks to tune and accelerate app performance
Who this book is for
Kai Sasaki works as a software engineer at Treasure Data. He engages in developing largescale distributed systems to make data valuable. His passion for creating artificial intelligence by processing large-scale data led him to the field of machine learning. He is one of the initial contributors to TensorFlow.js and keeps working to add new operators that are required for new types of machine learning models. Because of his work, he received the Google Open Source Peer Bonus in 2018.
Table of Contents
- Machine Learning for the Web
- Importing Pre-trained Models into TensorFlow.js
- TensorFlow.js Ecosystem
- Polynomial Regression
- Classification with Logistic Regression
- Unsupervised Learning
- Sequential Data Analysis
- Dimensionality Reduction
- Solving Markov decision problems
- Deploying Machine Learning Applications
- Tuning applications to achieve high performance
- Future Works around TensorFlow.js