Neural Networks and Deep Learning: A Textbook

Aggarwal, Charu C.

ISBN 10: 3319944622 / ISBN 13: 9783319944623

Verlag: Springer, 2018

Beschreibung

Prompt service guaranteed. Bestandsnummer des Verkäufers Clean3319944622

Bibliografische Details

Titel: Neural Networks and Deep Learning: A Textbook

Verlag: Springer

Erscheinungsdatum: 2018

Einband: Hardcover

Zustand: new

Über diesen Titel

Inhaltsangabe:

<P>THIS BOOK COVERS BOTH CLASSICAL AND MODERN MODELS IN DEEP LEARNING. THE PRIMARY FOCUS IS ON THE THEORY AND ALGORITHMS OF DEEP LEARNING. THE THEORY AND ALGORITHMS OF NEURAL NETWORKS ARE PARTICULARLY IMPORTANT FOR UNDERSTANDING IMPORTANT CONCEPTS, SO THAT ONE CAN UNDERSTAND THE IMPORTANT DESIGN CONCEPTS OF NEURAL ARCHITECTURES IN DIFFERENT APPLICATIONS. WHY DO NEURAL NETWORKS WORK? WHEN DO THEY WORK BETTER THAN OFF-THE-SHELF MACHINE-LEARNING MODELS? WHEN IS DEPTH USEFUL? WHY IS TRAINING NEURAL NETWORKS SO HARD? WHAT ARE THE PITFALLS? THE BOOK&NBSP; IS ALSO RICH IN DISCUSSING DIFFERENT APPLICATIONS IN ORDER TO GIVE THE PRACTITIONER A FLAVOR OF HOW NEURAL ARCHITECTURES ARE DESIGNED FOR DIFFERENT TYPES OF PROBLEMS. APPLICATIONS ASSOCIATED WITH MANY DIFFERENT AREAS LIKE RECOMMENDER SYSTEMS, MACHINE TRANSLATION, IMAGE CAPTIONING, IMAGE CLASSIFICATION, REINFORCEMENT-LEARNING BASED GAMING, AND TEXT ANALYTICS ARE COVERED.&NBSP;THE CHAPTERS OF THIS BOOK SPAN THREE CATEGORIES:</P> <P><B>THE BASICS OF NEURAL NETWORKS: </B>&NBSP;MANY TRADITIONAL MACHINE LEARNING MODELS CAN BE UNDERSTOOD AS SPECIAL CASES OF NEURAL NETWORKS.&NBSP; AN EMPHASIS IS PLACED IN THE FIRST TWO CHAPTERS ON UNDERSTANDING THE RELATIONSHIP BETWEEN TRADITIONAL MACHINE LEARNING AND NEURAL NETWORKS. SUPPORT VECTOR MACHINES, LINEAR/LOGISTIC REGRESSION, SINGULAR VALUE DECOMPOSITION, MATRIX FACTORIZATION, AND RECOMMENDER SYSTEMS ARE SHOWN TO BE SPECIAL CASES OF NEURAL NETWORKS. THESE METHODS ARE STUDIED TOGETHER WITH RECENT FEATURE ENGINEERING METHODS LIKE WORD2VEC.</P><P></P> <P><B>FUNDAMENTALS OF NEURAL NETWORKS:</B> A DETAILED DISCUSSION OF TRAINING AND REGULARIZATION IS PROVIDED IN CHAPTERS 3 AND 4. CHAPTERS 5 AND 6 PRESENT RADIAL-BASIS FUNCTION (RBF) NETWORKS AND RESTRICTED BOLTZMANN MACHINES.</P> <P><B>ADVANCED TOPICS IN NEURAL NETWORKS: </B>CHAPTERS 7 AND 8 DISCUSS RECURRENT NEURAL NETWORK

Críticas:

“The book recommends itself as a stepping-stone of the research-intensive area of deep learning and a worthy continuation of the previous textbooks written by the author ... . Thanks to its systematic and thorough approach complemented with the variety of resources (bibliographic and software references, exercises) neatly presented after each chapter, it is suitable for audiences of varied expertise or background.” (Irina Ioana Mohorianu, zbMATH 1402.68001, 2019)