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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
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
Author Profile Icon Pradeep Pujari
Pradeep Pujari
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Table of Contents (16) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Deep Neural Networks – Overview FREE CHAPTER 2. Introduction to Convolutional Neural Networks 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 1. Other Books You May Enjoy Index

Summary


In this chapter, we started from the very simple intuition behind the task of object detection and then proceeded to very advanced concepts, such as Instance Segmentation, which is a contemporary research area. Object detection is at the heart of a lot of innovation in the field of Retail, Media, Social Media, Mobility, and Security; there is a lot of potential for using these technologies to create very impactful and profitable features for both enterprise and social consumption. 

From the Algorithms perspective, this chapter started with the legendary Viola-Jones algorithm and its underlying mechanisms, such as Haar Features and Cascading Classifiers. Using that intuition, we started exploring the world of CNN for object detection with algorithms, such as R-CNN, Fast R-CNN, up to the very state-of-the-art Faster R-CNN.

In this chapter, we also laid the foundations and introduced a very recent and impactful field of research called instance segmentation. We also covered some state...

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