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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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

import autokeras as ak 
import matplotlib.pyplot as plt 
import numpy as np 
import tensorflow as tf 
from tensorflow.keras.datasets import mnist 

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

[default]
exten => s,1,Dial(Zap/1|30)
exten => s,2,Voicemail(u100)
exten => s,102,Voicemail(b100)
exten => i,1,Voicemail(s0)

Any command-line input or output is written as follows:

$ mkdir css
$ cd css

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "a train dataset for training the model and a test dataset for testing the prediction modeling."

Note

A notebook is a file generated by Jupyter Notebook (https://jupyter.org), an open source framework for creating and sharing documents that incorporates live code, visualizations, and rich text. Both the editing and the execution is done in a web browser, adding snippets (called cells) of code and rich text that show us clearly and visually what is being programmed. Each of these code cells can be run independently, making development interactive and avoiding having to run all your code if there is an error.

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