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Automated Machine Learning with AutoKeras

You're reading from Automated Machine Learning with AutoKeras Deep learning made accessible for everyone with just few lines of coding

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781800567641

Length 194 pages

Edition 1st Edition

Languages

Python

Tools

AutoKeras

Concepts

Deep Learning

Author (1):

Luis Sobrecueva

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: AutoML Fundamentals

2. Chapter 1: Introduction to Automated Machine Learning FREE CHAPTER

3. Chapter 2: Getting Started with AutoKeras

4. Chapter 3: Automating the Machine Learning Pipeline with AutoKeras

5. Section 2: AutoKeras in Practice

6. Chapter 4: Image Classification and Regression Using AutoKeras

7. Chapter 5: Text Classification and Regression Using AutoKeras

8. Chapter 6: Working with Structured Data Using AutoKeras

9. Chapter 7: Sentiment Analysis Using AutoKeras

10. Chapter 8: Topic Classification Using AutoKeras

11. Section 3: Advanced AutoKeras

12. Chapter 9: Working with Multimodal and Multitasking Data

13. Chapter 10: Exporting and Visualizing the Models

14. Other Books You May Enjoy

Summary

In this chapter, we learned about tensors, the main data structures for networks, some data preprocessing operations for neural networks, and the AutoKeras data formats that are supported, as well as its data-preprocessing utilities. Finally, we learned how to split a dataset in a quick and easy way. Now, you are ready to power your AutoKeras models in the most appropriate way.

In the next chapter, we will learn how AutoKeras works with images. We will also introduce some techniques we can use to extract specific characteristics from images and how to apply them.

The rest of the chapter is locked

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Authors (1)

Sobrecueva

Sobrecueva

Luis Sobrecueva is a senior software engineer and ML/DL practitioner currently working at Cabify. He has been a contributor to the OpenAI project as well as one of the contributors to the AutoKeras project.

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