**Autor: Kai Sasaki**

Broj strana: 296

ISBN broj: 9781838821739

Izdavač:
PACKT PUBLISHING

Godina izdanja: 2019.

Pregleda (30 dana / ukupno): **36 / 149**

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- 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

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.

- 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

**Table of contents**

**1 Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js ****2 Machine Learning for the Web **

Technical requirements

Why machine learning on the web?

Operation graphs

What is TensorFlow.js?

Installing TensorFlow.js

The low-level API

The Layers API

Summary

Questions

Further reading **3 Importing Pretrained Models into TensorFlow.js **

Technical requirements

The portable model format

Exporting a model from TensorFlow

Converting models using tfjs-converter

Loading the model into TensorFlow.js

Summary

Questions

Further reading **4 TensorFlow.js Ecosystem **

Technical requirements

Why high-level libraries?

Using existing models

Loading the data from various kinds of storage

Pose detection with ML5.js

Drawing cats with Magenta.js

XOR classification with machinelearn.js

Summary

Exercises

Further reading **5 Section 2: Real-World Applications of TensorFlow.js ****6 Polynomial Regression **

Technical requirements

What is polynomial regression?

Two-dimensional curve fitting

Summary

Questions

Further reading **7 Classification with Logistic Regression **

Technical requirements

Background of binary classification

What is logistic regression?

Classifying two-dimensional clusters

Summary

Questions

Further reading **8 Unsupervised Learning **

Technical requirements

What is unsupervised learning?

Learning how K-means works

Generalizing K-means with the EM algorithm

Clustering two groups in a 2D space

Summary

Exercise

Further reading **9 Sequential Data Analysis **

Technical requirements

What is Fourier transformation?

Cosine curve decomposition

Summary

Exercise

Further reading **10 Dimensionality Reduction **

Technical requirements

Why dimensionality reduction?

Understanding principal component analysis

Projecting 3D points into a 2D space with PCA

Word embedding

Summary

Exercise

Further reading **11 Solving the Markov Decision Process **

Technical requirements

Reinforcement learning

Solving the four-states environment

Summary

Exercise

Further reading **12 Section 3: Productionizing Machine Learning Applications with TensorFlow.js ****13 Deploying Machine Learning Applications **

Technical requirements

The ecosystem around the JavaScript platform

Module bundler

Deploying modules with GitHub Pages

Summary

Questions

Further reading **14 Tuning Applications to Achieve High Performance **

Technical requirements

The backend API of TensorFlow.js

Tensor management

Asynchronous data access

Profiling

Model visualization

Summary

Questions

Further reading **15 Future Work Around TensorFlow.js **

Technical requirements

Experimental backend implementations

AutoML edge helper

Summary

Questions

Further Reading **16 Other Books You May Enjoy **

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