Advances in Deep Learning (Hardcover, 1st ed. 2020)

This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Studies in Big Data, 57
Release date:	March 2019
First published:	2020
Authors:	M. Arif Wani • Farooq Ahmad Bhat • Saduf Afzal • Asif Iqbal Khan
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	149
Edition:	1st ed. 2020
ISBN-13:	978-981-13-6793-9
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	981-13-6793-0
Barcode:	9789811367939

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!