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Practical Convolutional Neural Networks

You're reading from   Practical Convolutional Neural Networks Implement advanced deep learning models using Python

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788392303
Length 218 pages
Edition 1st Edition
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Authors (3):
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Mohit Sewak Mohit Sewak
Author Profile Icon Mohit Sewak
Mohit Sewak
Md. Rezaul Karim Md. Rezaul Karim
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Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
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Pradeep Pujari
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Table of Contents (11) Chapters Close

Preface 1. Deep Neural Networks – Overview 2. Introduction to Convolutional Neural Networks FREE CHAPTER 3. Build Your First CNN and Performance Optimization 4. Popular CNN Model Architectures 5. Transfer Learning 6. Autoencoders for CNN 7. Object Detection and Instance Segmentation with CNN 8. GAN: Generating New Images with CNN 9. Attention Mechanism for CNN and Visual Models 10. Other Books You May Enjoy

Traditional, nonCNN approaches to object detection


Libraries such as OpenCV and some others saw rapid inclusion in the software bundles for Smartphones, Robotic projects, and many others, to provide detection capabilities of specific objects (face, smile, and so on), and Computer Vision like benefits, though with some constraints even before the prolific adoption of CNN.

CNN-based research in this area of object detection and Instance Segmentation provided many advancements and performance enhancements to this field, not only enabling large-scale deployment of these systems but also opening avenues for many new solutions. But before we plan to jump into CNN based advancements, it will be a good idea to understand how the challenges cited in the earlier section were answered to make object detection possible in the first place (even with all the constraints), and then we will logically start our discussion about the different researchers and the application of CNN to solve other problems that...

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