Deep Learning: Algorithms and Applications
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
1133231840
Deep Learning: Algorithms and Applications
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
199.99 In Stock
Deep Learning: Algorithms and Applications

Deep Learning: Algorithms and Applications

Deep Learning: Algorithms and Applications

Deep Learning: Algorithms and Applications

Overview

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

Product Details

ISBN-13: 9783030317591
Publisher: Springer International Publishing
Publication date: 10/24/2019
Series: Studies in Computational Intelligence , #865
Edition description: 1st ed. 2020
Pages: 360
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

Preface.- Chapter 1. Activation Functions.- Chapter 2. Adversarial Examples in Deep Neural Networks: An Overview.- Chapter 3. Representation Learning in Power Time Series Forecasting, etc.
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