Hands-On Machine Learning with TensorFlow.js: A guide to building ML applications integrated with web technology using the TensorFlow.js library
Authors: Kai Sasaki
ISBN-10: 1838821732
ISBN-13: 9781838821739
Publication Date 出版日期: 2019-11-27
Print Length 页数: 296 pages
Book Description
By finelybook
Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively
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
Contents
Preface
Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
Chapter 1: Machine Learning for the Web
Chapter 2: Importing Pretrained Models into TensorFlow.js
Chapter 3: TensorFlow.js Ecosystem
Section 2: Real-World Applications of TensorFlow.js
Chapter 4: Polynomial Regression
Chapter 5: Classification with Logistic Regression
Chapter 6: Unsupervised Learning
Chapter 7: Sequential Data Analysis
Chapter 8: Dimensionality Reduction
Chapter 9: Solving the Markov Decision Process
Section 3: Productionizing Machine Learning Applications with TensorFlow.js
Chapter 10: Deploying Machine Learning Applications
Chapter 11: Tuning Applications to Achieve High Performance
Chapter 12: Future Work Around TensorFlow.js
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Index
