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Hands-On Machine Learning with TensorFlow.js

You're reading from   Hands-On Machine Learning with TensorFlow.js A guide to building ML applications integrated with web technology using the TensorFlow.js library

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838821739
Length 296 pages
Edition 1st Edition
Languages
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Author (1):
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Kai Sasaki Kai Sasaki
Author Profile Icon Kai Sasaki
Kai Sasaki
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
2. Machine Learning for the Web FREE CHAPTER 3. Importing Pretrained Models into TensorFlow.js 4. TensorFlow.js Ecosystem 5. Section 2: Real-World Applications of TensorFlow.js
6. Polynomial Regression 7. Classification with Logistic Regression 8. Unsupervised Learning 9. Sequential Data Analysis 10. Dimensionality Reduction 11. Solving the Markov Decision Process 12. Section 3: Productionizing Machine Learning Applications with TensorFlow.js
13. Deploying Machine Learning Applications 14. Tuning Applications to Achieve High Performance 15. Future Work Around TensorFlow.js 16. Other Books You May Enjoy

Loading the data from various kinds of storage

The training dataset is a fundamental building block of the machine learning pipeline. Importing the variety of datasets without difficulty surely improves our productivity. tfjs-data is an analog library of tf.data in TensorFlow. It makes data loading simply by providing a uniform way of accessing the training data. As the library is contained by the core library of TensorFlow.js, tfjs-data will be available when you install @tensorflow/tfjs:

yarn add @tensorflow/tfjs

Data sources

TensorFlow.js provides you with a unified access layer for various kinds of data sources represented by tf.data.Dataset. The data structure is an ordered collection of each sample that allows us to...

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