Convolutional Neural Networks in Visual Computing: A Concise Guide

Ragav Venkatesan, Baoxin Li

CRC Press
Published September 1, 2017
Reference - 168 Pages
ISBN 9781498770392 - CAT# K29360
Series: Data-Enabled Engineering

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  • Comprehensive enough to cover what is needed to develop and implement CNNs
  • Self-contained for audiences outside the computer science research domain, e.g., audiences in industry
  • Easy to understand and well-illustrated with small examples and case studies along with code-snippets and data sets
  • Helpful for early graduate students and college seniors who want to explore the field with some hands-on
  • A single source to self-learn concepts, methods and software tools required to completely and single-handedly implement state-of-the-art CNN systems


This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.


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