Deep Learning: A Beginners' Guide

This book provides a comprehensive resource for understanding deep learning (DL), which is an important aspect of data science and has applications in various domains. It covers the theory and design of state-of-the-art deep learning models, discusses problem-solving techniques, and explores the theoretical basis for major algorithms and approaches. It also covers enhancement techniques and performance evaluation techniques and is suitable for beginners and researchers alike.

Format: Hardback
Length: 184 pages
Publication date: 16 October 2023
Publisher: Taylor & Francis Ltd

Deep learning (DL) is a crucial component of data science, encompassing predictive modeling and has a wide range of applications across various domains such as finance, transportation, healthcare, automanufacturing, and advertising. The design of deep learning models, which are based on artificial neural networks, is influenced by the structure and operation of the human brain. This comprehensive book aims to provide a solid understanding of the techniques involved in deep learning.

Key features:

Comprehensive coverage of state-of-the-art deep learning models for real-world applications

In-depth explanation of concepts and terminology in problem-solving with deep learning

Theoretical basis for major algorithms and approaches in deep learning

Discussion of enhancement techniques for deep learning models

Identification of performance evaluation techniques for deep learning models

The book covers the entire process flow of deep learning, from awareness of widely used models to their practical implementation. It serves as a valuable resource for beginners who wish to grasp the associated concepts and techniques. Additionally, it is suitable for undergraduate and postgraduate students, engineers, and researchers who are embarking on their journey into the field of deep learning.

Weight: 440g
Dimension: 160 x 243 x 19 (mm)
ISBN-13: 9781032473246