Deep Learning Foundations (1st ed. 2023)

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	July 2023
First published:	2023
Authors:	Taeho Jo
Dimensions:	235 x 155mm (L x W)
Pages:	426
Edition:	1st ed. 2023
ISBN-13:	978-3-03-132878-7
Categories:	Books
LSN:	3-03-132878-7
Barcode:	9783031328787

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