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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

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781800567641
Length 194 pages
Edition 1st Edition
Languages
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Author (1):
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Luis Sobrecueva Luis Sobrecueva
Author Profile Icon Luis Sobrecueva
Luis Sobrecueva
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Toc

Table of Contents (15) Chapters Close

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

What this book covers

Chapter 1, Introduction to Automated Machine Learning, covers the main concepts of automated machine learning with an overview of the types of AutoML methods and its software systems.

Chapter 2, Getting Started with AutoKeras, covers everything you need in order to get started with AutoKeras and put it into practice with the help of a foundational, well explained code example.

Chapter 3, Automating the Machine Learning Pipeline with AutoKeras, explains the standard machine learning pipeline, explains how to automate such a pipeline with AutoKeras, and describes the main data preparation best practices to apply before training a model.

Chapter 4, Image Classification and Regression Using AutoKeras, focuses on the use of AutoKeras applied to images by creating more complex and powerful image recognizers, examining how they work, and seeing how to fine-tune them to improve their performance.

Chapter 5, Text Classification and Regression Using AutoKeras, focuses on the use of AutoKeras to work with text (sequences of words). This chapter also explains what recurrent neural networks are and how they work.

Chapter 6, Working with Structured Data Using AutoKeras, enables you to explore a structured dataset, transform it, and use it as a data source for specific models, as well as create your own classification and regression models to solve tasks based on structured data.

Chapter 7, Sentiment Analysis Using AutoKeras, uses a text classifier to extract sentiments from text data and applies the concepts of text classification in a practical way by implementing the sentiment predictor.

Chapter 8, Topic Classification Using AutoKeras, focuses on the practical aspects of the text-based tasks learned in the previous chapters. It teaches you how to create a topic classifier with AutoKeras and then apply it to any topic or category-based dataset.

Chapter 9, Working with Multi-Modal Data and Multi-Task, covers the use of the AutoModel API to show how to handle multimodal and multitasking data.

Chapter 10, Exporting and Visualizing the Models, teaches you to export and import AutoKeras models and visualize graphically, as well as in real time, what is happening during the training of our models.

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