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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from  Applied Deep Learning and Computer Vision for Self-Driving Cars

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Pages 332 pages
Edition 1st Edition
Languages
Authors (2):
Sumit Ranjan Sumit Ranjan
Profile icon Sumit Ranjan
Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Profile icon Dr. S. Senthamilarasu
View More author details

Table of Contents (18) Chapters

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

Keras for deep learning

Deep learning started to gain popularity a couple of years ago when AlexNet, a convolutional neural network (CNN) designed by Alex Krizhevsky and published with Ilya Sutskever and doctoral adviser Geoffrey Hinton, also referred to as the godfather of deep learning, was created. AlexNet blew away the ImageNet Large Scale Visual Recognition Challenge on 30 September 2012. Their deep neural network was significantly better than all the other submissions. Architectures such as AlexNet have revolutionized the field of computer vision. In the following diagram, you can see the top five predictions for the visual challenge where AlexNet emerged victorious:

Fig 3.2: Visual Recognition Challenge 2012

Because deep learning requires lots of GPU computation and data, people began to take notice and implemented their own deep neural networks for different tasks, resulting in a deep learning library.

Theano was one of the first widely adopted deep learning...

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