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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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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

Summary

In this chapter, we introduced how the regression problem can be solved by TensorFlow.js. The regression problem is common in any machine learning application. Now, you know how to write an application that solves this type of problem.

You have learned what the regression problem is and how the polynomial regression model can be applied to solve the problem. Polynomial regression is a simple mathematical model that predicts the continuous target value. However, it can show a pretty good result if we use a well-tuned optimizer such as Adam. Therefore, you should also learn how iterative optimization works. We will look at this continuously throughout this book.

Regression problems can appear in any kind of format, but for simplicity, we tried to solve the sine curve fitting problem as an example of a regression problem. You saw how your model fit the target curve properly...

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