Cantitate/Preț
Produs

Deep Learning: Algorithms and Applications (Studies in Computational Intelligence, nr. 865)

Editat de Witold Pedrycz, Shyi-Ming Chen
Notă GoodReads:
en Limba Engleză Paperback – 04 Nov 2020
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.
Citește tot Restrânge
Toate formatele și edițiile
Toate formatele și edițiile Preț Express
Paperback (2) 66413 lei  3-5 săpt. +6270 lei  10-18 zile
  Springer – 04 Nov 2020 66413 lei  3-5 săpt. +6270 lei  10-18 zile
  Springer – 13 Nov 2020 67108 lei  7-9 săpt. +40599 lei  10-18 zile
Hardback (2) 68082 lei  7-9 săpt. +26796 lei  4-10 zile
  Springer – 13 Nov 2019 68082 lei  7-9 săpt. +26796 lei  4-10 zile
  Springer – 04 Nov 2019 68234 lei  7-9 săpt. +34285 lei  10-18 zile

Din seria Studies in Computational Intelligence

Preț: 66413 lei

Preț vechi: 83015 lei
-20%

Puncte Express: 996

Preț estimativ în valută:
12797 12369$ 11544£

Carte disponibilă

Livrare economică 18 octombrie-01 noiembrie
Livrare express 07-15 octombrie pentru 7269 lei

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783030317621
ISBN-10: 3030317625
Pagini: 360
Ilustrații: XII, 360 p. 171 illus., 139 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.57 kg
Ediția: 1st ed. 2020
Editura: Springer
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării: Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

Provides a comprehensive and up-to-date overview of deep learning by discussing a range of methodological and algorithmic issues
Addresses implementations and case studies, identifying the best design practices and assessing business models and methodologies encountered in industry, health care, science, administration, and business
Serves as a unique and well-structured reference resource for graduate and senior undergraduate students in areas such as computational intelligence, pattern recognition, computer vision, knowledge acquisition and representation, and knowledge-based systems