Luis Sobrecueva - Automated Machine Learning with AutoKeras: Deep learning made accessible for everyone with just few lines of coding
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Create better and easy-to-use deep learning models with AutoKeras
Key Features- Design and implement your own custom machine learning models using the features of AutoKeras
- Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis
- Get familiar with advanced concepts as multi-modal, multi-task, and search space customization
AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you.
This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, youll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions.
By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company.
What you will learn- Set up a deep learning workstation with TensorFlow and AutoKeras
- Automate a machine learning pipeline with AutoKeras
- Create and implement image and text classifiers and regressors using AutoKeras
- Use AutoKeras to perform sentiment analysis of a text, classifying it as negative or positive
- Leverage AutoKeras to classify documents by topics
- Make the most of AutoKeras by using its most powerful extensions
This book is for machine learning and deep learning enthusiasts who want to apply automated ML techniques to their projects. Prior basic knowledge of Python programming and machine learning is expected to get the most out of this book.
Table of Contents- Introduction to Automated Machine Learning
- Getting Started with AutoKeras
- Automating the Machine Learning Pipeline with AutoKeras
- Image Classification and Regression Using AutoKeras
- Text Classification and Regression Using AutoKeras
- Working with Structured Data Using AutoKeras
- Sentiment Analysis Using AutoKeras
- Topic Classification Using AutoKeras
- Working with Multi-Modal Data and Multi-Task
- Exporting and Visualizing the Models
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Deep learning made accessible for everyone with just few lines of coding
Luis Sobrecueva
BIRMINGHAMMUMBAI
Copyright 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Group Product Manager: Kunal Parikh
Publishing Product Manager: Reshma Raman
Senior Editor: Mohammed Yusuf Imaratwale
Content Development Editor: Sean Lobo
Technical Editor: Sonam Pandey
Copy Editor: Safis Editing
Project Coordinator: Aparna Ravikumar Nair
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Production Designer: Prashant Ghare
First published: May 2021
Production reference: 1210421
Published by Packt Publishing Ltd.
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ISBN 978-1-80056-764-1
www.packt.com
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.
Satya Kesav is a computer science graduate, machine learning enthusiast, and software engineer interested in building end-to-end machine learning products at scale. He has 2+ years of experience in this field, having worked on interesting products including Google Search and YouTube, as well as for an NLP-based start-up and interesting products including Google Search and YouTube. He was an early contributor to the AutoKeras deep learning library, which is now collaborated with Google Brain. He has published four papers and two patents in his career, working in a multitude of fields in computer science.
Anton Hromadskyi has designed data schemas for multiple projects, has configured migration/ETL, has written a lot of algorithms relating to data preparation and feature engineering, has developed and integrated BI, and has implemented prediction models, trading bots, and data processors for the marketing platform. He has applied decision trees, regression, neural networks, anomaly detection, PCA, and ICA and developed ensembles of stacked models for AI solutions, along with a state-action model for a chatbot. He has accepted a legacy AI project without documentation for two weeks before delivery which proved to be a success. Special thanks to Aparna for being patient.
Can deep learning be accessible to everyone? Without a doubt, this is the objective that the cloud services offered by giants such as Google or Amazon are trying to achieve. Google AutoML and Amazon ML services are cloud-based services that make it easy for developers of all skill levels to use machine learning technology. AutoKeras is the free open source alternative and, as we'll see soon, a fantastic framework.
When faced with a deep learning problem, the choice of an architecture or the configuration of certain parameters when creating a model usually comes from the intuition of the data scientist, based on years of study and experience.
In my case, being a software engineer without a broad background in data science, I have always looked for methods to automate this part, using different search algorithms (grid, evolutionary, or Bayesian) to explore the different variables that make up a model.
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