Computer Vision Using Deep Learning

Neural Network Architectures with Python and Keras

Authors: Verdhan, Vaibhav

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  • Implement Deep Learning solutions on your own systems to bridge the gap between theory and practice
  • Examine the inner workings of the codes and libraries that make Deep Learning applications work
  • Create solutions for computer vision design using Keras and TensorFlow
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eBook 26,99 €
price for Spain (gross)
  • Due: March 30, 2021
  • ISBN 978-1-4842-6616-8
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
Softcover 36,39 €
price for Spain (gross)
  • Due: February 15, 2021
  • ISBN 978-1-4842-6615-1
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. 

This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments.

Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. 

What You'll Learn

  • Examine deep learning code and concepts to apply guiding principals to your own projects
  • Classify and evaluate various architectures to better understand your options in various use cases
  • Go behind the scenes of basic deep learning functions to find out how they work

Who This Book Is For

Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.

About the authors

Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and retail, telecom, manufacturing, health-care and utilities domain. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist. 

Table of contents (8 chapters)

Table of contents (8 chapters)

Buy this book

eBook 26,99 €
price for Spain (gross)
  • Due: March 30, 2021
  • ISBN 978-1-4842-6616-8
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
Softcover 36,39 €
price for Spain (gross)
  • Due: February 15, 2021
  • ISBN 978-1-4842-6615-1
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Computer Vision Using Deep Learning
Book Subtitle
Neural Network Architectures with Python and Keras
Authors
Copyright
2021
Publisher
Apress
Copyright Holder
Vaibhav Verdhan
eBook ISBN
978-1-4842-6616-8
DOI
10.1007/978-1-4842-6616-8
Softcover ISBN
978-1-4842-6615-1
Edition Number
1
Number of Pages
XXI, 308
Number of Illustrations
36 b/w illustrations, 115 illustrations in colour
Topics