Angular and Machine Learning Pocket Primer
(eBook)

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Published:
[United States] : Packt Publishing, 2024.
Format:
eBook
Content Description:
1 online resource (278 pages)
Status:

Description

As part of the best-selling *Pocket Primer* series, this book introduces readers to basic machine learning concepts and integrates them into Angular applications. It offers a fast-paced introduction to essential machine learning features and an overview of popular classifiers. Covering Angular functionality, basic machine learning concepts, and key classification algorithms, the book includes numerous code samples and figures. Topics such as TensorFlow and Keras are also explored. The book begins with a quick introduction to Angular, followed by UI controls, user input, and forms and services. As you progress, you will delve into machine learning, working with classifiers, and integrating TensorFlow.js with Angular. These foundational topics ensure a comprehensive grasp of both Angular and machine learning principles, equipping you with practical skills for developing sophisticated applications. Understanding these concepts is crucial for enhancing Angular projects with machine learning capabilities. This book transitions you from a novice to a proficient developer, equipped with the knowledge and tools needed to create advanced applications. Companion files with source code and color figures enhance the learning experience, making this book an invaluable resource for integrating machine learning with Angular.

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ISBN:
9781836645603, 1836645600

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Instant title available through hoopla.
Description
As part of the best-selling *Pocket Primer* series, this book introduces readers to basic machine learning concepts and integrates them into Angular applications. It offers a fast-paced introduction to essential machine learning features and an overview of popular classifiers. Covering Angular functionality, basic machine learning concepts, and key classification algorithms, the book includes numerous code samples and figures. Topics such as TensorFlow and Keras are also explored. The book begins with a quick introduction to Angular, followed by UI controls, user input, and forms and services. As you progress, you will delve into machine learning, working with classifiers, and integrating TensorFlow.js with Angular. These foundational topics ensure a comprehensive grasp of both Angular and machine learning principles, equipping you with practical skills for developing sophisticated applications. Understanding these concepts is crucial for enhancing Angular projects with machine learning capabilities. This book transitions you from a novice to a proficient developer, equipped with the knowledge and tools needed to create advanced applications. Companion files with source code and color figures enhance the learning experience, making this book an invaluable resource for integrating machine learning with Angular.
System Details
Mode of access: World Wide Web.

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Citations

APA Citation (style guide)

Mercury Learning and Information. (2024). Angular and Machine Learning Pocket Primer. Packt Publishing.

Chicago / Turabian - Author Date Citation (style guide)

Mercury Learning and Information. 2024. Angular and Machine Learning Pocket Primer. Packt Publishing.

Chicago / Turabian - Humanities Citation (style guide)

Mercury Learning and Information, Angular and Machine Learning Pocket Primer. Packt Publishing, 2024.

MLA Citation (style guide)

Mercury Learning and Information. Angular and Machine Learning Pocket Primer. Packt Publishing, 2024.

Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.

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Grouped Work ID:
29512766-6731-1410-9af2-17f502fbd343
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Hoopla Extract Information

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duration
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abridged
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purchaseModelINSTANT
dateLastUpdatedNov 15, 2024 08:29:28 PM

Record Information

Last File Modification TimeDec 02, 2024 11:24:03 PM
Last Grouped Work Modification TimeDec 02, 2024 10:24:25 PM

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