Fr. 143.80

The Journey from Artificial to Convolutional Neural Network

English · Paperback / Softback

Shipping usually within 2 to 3 weeks (title will be printed to order)

Description

Read more










This book has unfolded the reasons of the successful adoption of deep learning-based convolutional neural networks over machine learning- based artificial neural networks. The book has provided sufficient knowledge of the topics like Pandas and Numpy in Python programing language before switching to the core implementation of any Artificial Intelligence (AI) based algorithm. Thereafter, the theoretical concept of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) is discussed with step-by-step implementation in Python programing language. All the layers of the CNN model have been discussed in detail. Separate chapters are dedicated to the process of convolution, activation function, and pooling representing the theoretical concepts with practical implementation.

This book is written for anyone interested in acquiring theoretical and practical knowledge of Artificial Intelligence. Students pursuing graduation and post-graduation in engineering in computer science can follow this book. In addition, this book will be very useful for candidates who are interested in the Artificial Intelligence-related research.

For more details, please visit https://centralwestpublihing.com

Product details

Authors Amit Verma
Publisher Central West Publishing
 
Languages English
Product format Paperback / Softback
Released 01.05.2023
 
EAN 9781922617422
ISBN 978-1-922617-42-2
No. of pages 140
Dimensions 152 mm x 229 mm x 8 mm
Weight 215 g
Series Computing
Subject Natural sciences, medicine, IT, technology > IT, data processing > IT

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.