國立虎尾科技大學 |

Essentials of deep learning and AI = experience unsupervised learning, autoencoders, feature engineering and time series analysis with tensorflow, keras, and scikit-learn /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Essentials of deep learning and AI / Shashidhar Soppin, Manjunath Ramachandra, B N Chandrashekar.
Reminder of title:	experience unsupervised learning, autoencoders, feature engineering and time series analysis with tensorflow, keras, and scikit-learn /
Author:	Soppin, Shashidhar.
other author:	Chandrashekar, B N,
Description:	1 online resource (395 pages)
Subject:	COMPUTERS / Data Science / Machine Learning. -
Online resource:	https://portal.igpublish.com/iglibrary/search/BPB0000287.html
ISBN:	9789391030025

Essentials of deep learning and AI = experience unsupervised learning, autoencoders, feature engineering and time series analysis with tensorflow, keras, and scikit-learn /
Soppin, Shashidhar.

Essentials of deep learning and AI experience unsupervised learning, autoencoders, feature engineering and time series analysis with tensorflow, keras, and scikit-learn /[electronic resource] ：Shashidhar Soppin, Manjunath Ramachandra, B N Chandrashekar. - 1 online resource (395 pages)

Includes bibliographical references and index.

Essentials of deep learning and AI : experience unsupervised learning, autoencoders, feature engineering and time series analysis with tensorflow, keras, and scikit-learn -- Foreword -- Dedication -- About the Authors -- About the Reviewer -- Acknowledgement -- Preface -- Table of Contents -- Chapter 1: Introduction -- Chapter 2: Supervised Machine Learning -- Chapter 3: System Analysis with Machine Learning/ Un-Supervised Learning -- Chapter 4: Feature Engineering -- Chapter 5: Classification, Clustering, Association Rules, and Regression -- Chapter 6: Time Series Analysis -- Chapter 7: Data Cleanup, Characteristics and Feature Selection -- Chapter 8: Ensemble Model Development -- Chapter 9: Design with Deep Learning -- Chapter 10: Design with Multi Layered Perceptron (MLP) -- Chapter 11: Long Short Term Memory Networks -- Chapter 12: Autoencoders -- Chapter 13: Applications of Machine Learning and Deep Learning -- Chapter 14: Emerging and Future Technologies -- Index.

Access restricted to authorized users and institutions.

This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment.

Mode of access: World Wide Web.

ISBN: 9789391030025Subjects--Topical Terms:

1483854
COMPUTERS / Data Science / Machine Learning.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: Q325.73

Dewey Class. No.: 006.3/1

Essentials of deep learning and AI = experience unsupervised learning, autoencoders, feature engineering and time series analysis with tensorflow, keras, and scikit-learn /
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505 0 # $a Essentials of deep learning and AI : experience unsupervised learning, autoencoders, feature engineering and time series analysis with tensorflow, keras, and scikit-learn -- Foreword -- Dedication -- About the Authors -- About the Reviewer -- Acknowledgement -- Preface -- Table of Contents -- Chapter 1: Introduction -- Chapter 2: Supervised Machine Learning -- Chapter 3: System Analysis with Machine Learning/ Un-Supervised Learning -- Chapter 4: Feature Engineering -- Chapter 5: Classification, Clustering, Association Rules, and Regression -- Chapter 6: Time Series Analysis -- Chapter 7: Data Cleanup, Characteristics and Feature Selection -- Chapter 8: Ensemble Model Development -- Chapter 9: Design with Deep Learning -- Chapter 10: Design with Multi Layered Perceptron (MLP) -- Chapter 11: Long Short Term Memory Networks -- Chapter 12: Autoencoders -- Chapter 13: Applications of Machine Learning and Deep Learning -- Chapter 14: Emerging and Future Technologies -- Index.
506 # $a Access restricted to authorized users and institutions.
520 3 $a This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment.
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