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Automated machine learning with AutoKeras : deep learning made accessible for everyone with just few lines of coding /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt,
[2021]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 015 | |a GBC177896 |2 bnb | ||
| 016 | 7 | |a 020196379 |2 Uk | |
| 020 | |a 9781800561816 |q electronic book | ||
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| 020 | |z 9781800567641 |q paperback | ||
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| 037 | |a 9781800561816 |b Packt Publishing Pvt. Ltd | ||
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| 050 | 4 | |a Q325.5 |b .S63 2021 | |
| 082 | 0 | 4 | |a 006.3/1 |2 23 |
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| 100 | 1 | |a Sobrecueva, Luis, |e author. | |
| 245 | 1 | 0 | |a Automated machine learning with AutoKeras : |b deep learning made accessible for everyone with just few lines of coding / |c Luis Sobrecueva. |
| 264 | 1 | |a Birmingham : |b Packt, |c [2021] | |
| 300 | |a 1 online resource (xi, 176 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on February 17, 2022). | ||
| 520 | |a 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 Book DescriptionAutoKeras 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 Who this book is for 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. | ||
| 505 | 0 | |a 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. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 776 | 0 | 8 | |i Print version: |a Sobrecueva, Luis. |t Automated Machine Learning with AutoKeras. |d Birmingham : Packt Publishing, Limited, ©2021 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781800567641/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH38629049 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6611973 | ||
| 938 | |a EBSCOhost |b EBSC |n 2916829 | ||
| 994 | |a 92 |b IZTAP | ||