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Key benefits

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

Description

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, you'll 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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

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Redeem a companion digital copy on all Print orders

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Frequently bought together

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Automated Machine Learning with AutoKeras

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Total R$ 764.97

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Amazon verified reviews

Payton Soicher Aug 04, 2021

I'm a data scientist and I had never heard of AutoKeras before this book. It did a great job explaining what it is, why you would use it, and how to interpret each model that you use. I really enjoyed it

Amazon Verified review

Syed Subhan Ahsen Jul 01, 2021

The book is a thorough guide about all the aspects of Automated Machine Learning using AutoKeras. Anybody who is currently in ML and wants to dive into AutoML world, AutoKeras library provides all the functionalities, starting from building/tuning sophisticated models and later visualizing those in a single open-source API. Furthermore, AutoKeras helps leveraging computational resources by using GPU. This book is a brilliant resource to understand and deploy AutoML models in all major application areas like image/text classification and regression, sentiment analysis, structured data regression etc.The following features of the book are extremely helpful-Provides hands on experience as all the examples are driven by Jupyter Notebooks with a git-hub link-Examples are often supplemented with ‘to the point’ explanation which aids in understanding technical aspects of the problem.-In the context of data regression, this book provides multitask examples (prediction of multiple output vectors with the same input data) which correlates well with most of the practical regression use cases.-Separate section is included for visualizing and tracking of the deployed models using ClearML (trains)In short, a very useful book for both new and experienced users to understand AutoKeras in a more practical way.

M. Petrey May 21, 2021

As someone who is more familiar with traditional machine learning pipelines but not AutoML, I found this book a great introduction to AutoML and AutoKeras. I appreciated that the code examples were all in Jupyter notebooks, making them very easy to run locally or on Google Colab. Although many of the examples are from data sets folks will be familiar with (MNIST, Titanic, etc.) I appreciated that familiarity when using the new tools - the data sets didn't feel like tropes like they sometimes can. I have less experience with text analytics, and I especially liked the examples with sentiment analysis and topic clustering. The consistent structure across chapters was helpful when approaching the new content. If you're already familiar with automated machine learning, I think you'll still find helpful pieces in this book, either on new topics, visualizing models, or working with more advanced model options.

Dr. Sreenivas Bhattiprolu Jun 24, 2021

Machine learning is a rapidly evolving technology that addresses challenges in many fields including, sciences, engineering, business, arts, and humanities. Unfortunately, it is mostly accessible to advanced coders coming from strong computer sciences backgrounds. Machine learning is still not part of the core curriculum for students in these fields. For example, biologists trying to understand sub-cellular features receive training in collecting the best microscope images. But, when it comes to analyzing those images, they tend to spend a significant amount of their valuable research time doing manual tasks that can be easily automated using Machine Learning.Google AI released AutoML to enable coders with limited machine learning skills to automatically train neural networks on their datasets. AutoKeras is an AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. AutoKeras democratizes machine learning and makes it easy to train neural networks with only a few lines of code in python. In fact, I was amazed by its simplicity when I discovered it in 2020 so I made a couple of training videos for my YouTube channel (@DigitalSreeni).This book provides broader insights into AutoKeras. It summarizes the essence of AutoKeras by walking the reader through a variety of examples including image classification, text classification, and sentiment analysis.I liked the clear explanation of pain points in machine learning workflows and how AutoML addresses these pain points. I particularly liked the comparison of various AutoML tools and why the authors chose to focus on the AutoKeras tool. I appreciate the coverage of AutoKeras installation on Google Colab. Many readers will not have access to GPU for deep learning and Colab makes it freely accessible. The text classification chapter is noteworthy, especially the introduction of common terminology used in tokenization and vectorization. The accompanying exercises in all chapters demonstrate the ability of AutoKeras in simplifying the machine learning process. Finally, I enjoyed the explanation of TensorBoard, a much-needed visualization toolkit to evaluate model performance.My only critical feedback would be about the use of standard data sets such as MNIST and CIFAR-10. Loading custom data is a challenge for novice machine learning engineers, the targeted user base for AutoKeras. It would have been beneficial for the reader to understand the advantage of AutoKeras while working on custom datasets. Also, I believe the book missed an opportunity to make the reader realize the pain of working without AutoKeras. A walk-through exercise without and with AutoKeras would have added icing to the cake.In summary, I recommend this book to new and mid-level machine learning coders. I even advise advanced coders to give it a look as it may help you speed up some of your work. Considering that the book is under 200 pages, it makes an easy read.

Ignasi Fosch May 21, 2021

Those interested in ML and, more specifically, AutoML will probably be already aware of AutoKeras. Luis is an AutoKeras contributor and this book of his is a good dive in into AutoKeras' and the process it helps to implement. It's also interesting to see how this can help people not willing to go into AutoML cloud services build up their own platform.For those who want to get introduced into AutoML, this is a pretty recommendable book. While it requires some prior knowledge on ML, DL, and generic open source projects usage, it's still providing some introduction.

Automated Machine Learning with AutoKeras: Deep learning made accessible for everyone with just few lines of coding

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