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Applied deep learning = tools, techn...

Fergus, Paul.

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Applied deep learning = tools, techniques, and implementation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied deep learning/ by Paul Fergus, Carl Chalmers.
Reminder of title:	tools, techniques, and implementation /
Author:	Fergus, Paul.
other author:	Chalmers, Carl.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xxvii, 341 p. :ill., digital ;24 cm.
[NT 15003449]:	Part 1 Introduction and Overview -- Introduction -- Part 2 Foundations of Mashine Learning -- Fundamentals of Machine Learning -- Supervised Learning -- Un-Supervised Learning -- Performance Evaluation Metrics -- Part 3 Deep Learning Concepts and Techniques -- Introduction to Deep Learning -- Image Classification and Object Detection -- Deep Learning Techniques for Time Series Modelling -- Natural Language Processing -- Deep Generative Models -- Deep Reinforcement Learning -- Part 4 Enterprise Machine Learning -- Accelerated Machine Learning -- Deploying and Hosting Machine Learning Models -- Enterprise Machine Learning Serving.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-3-031-04420-5
ISBN:	9783031044205

Applied deep learning = tools, techniques, and implementation /
Fergus, Paul.

Applied deep learningtools, techniques, and implementation /[electronic resource] :by Paul Fergus, Carl Chalmers. - Cham :Springer International Publishing :2022. - xxvii, 341 p. :ill., digital ;24 cm. - Computational intelligence methods and applications,2510-1773. - Computational intelligence methods and applications..

Part 1 Introduction and Overview -- Introduction -- Part 2 Foundations of Mashine Learning -- Fundamentals of Machine Learning -- Supervised Learning -- Un-Supervised Learning -- Performance Evaluation Metrics -- Part 3 Deep Learning Concepts and Techniques -- Introduction to Deep Learning -- Image Classification and Object Detection -- Deep Learning Techniques for Time Series Modelling -- Natural Language Processing -- Deep Generative Models -- Deep Reinforcement Learning -- Part 4 Enterprise Machine Learning -- Accelerated Machine Learning -- Deploying and Hosting Machine Learning Models -- Enterprise Machine Learning Serving.

This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks. The book covers all of the key conceptual aspects of the field and provides a foundation for all interested parties to develop their own artificial intelligence applications.

ISBN: 9783031044205

Standard No.: 10.1007/978-3-031-04420-5doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .F47 2022

Dewey Class. No.: 006.31

Applied deep learning = tools, techniques, and implementation /
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490 1 $a Computational intelligence methods and applications, $x 2510-1773
505 0 $a Part 1 Introduction and Overview -- Introduction -- Part 2 Foundations of Mashine Learning -- Fundamentals of Machine Learning -- Supervised Learning -- Un-Supervised Learning -- Performance Evaluation Metrics -- Part 3 Deep Learning Concepts and Techniques -- Introduction to Deep Learning -- Image Classification and Object Detection -- Deep Learning Techniques for Time Series Modelling -- Natural Language Processing -- Deep Generative Models -- Deep Reinforcement Learning -- Part 4 Enterprise Machine Learning -- Accelerated Machine Learning -- Deploying and Hosting Machine Learning Models -- Enterprise Machine Learning Serving.
520 $a This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks. The book covers all of the key conceptual aspects of the field and provides a foundation for all interested parties to develop their own artificial intelligence applications.
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