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Hands-On Vision and Behavior for Self-Driving Cars

You're reading from   Hands-On Vision and Behavior for Self-Driving Cars Explore visual perception, lane detection, and object classification with Python 3 and OpenCV 4

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781800203587
Length 374 pages
Edition 1st Edition
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Authors (2):
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Krishtof Korda Krishtof Korda
Author Profile Icon Krishtof Korda
Krishtof Korda
Luca Venturi Luca Venturi
Author Profile Icon Luca Venturi
Luca Venturi
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Table of Contents (17) Chapters Close

Preface 1. Section 1: OpenCV and Sensors and Signals
2. Chapter 1: OpenCV Basics and Camera Calibration FREE CHAPTER 3. Chapter 2: Understanding and Working with Signals 4. Chapter 3: Lane Detection 5. Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks
6. Chapter 4: Deep Learning with Neural Networks 7. Chapter 5: Deep Learning Workflow 8. Chapter 6: Improving Your Neural Network 9. Chapter 7: Detecting Pedestrians and Traffic Lights 10. Chapter 8: Behavioral Cloning 11. Chapter 9: Semantic Segmentation 12. Section 3: Mapping and Controls
13. Chapter 10: Steering, Throttle, and Brake Control 14. Chapter 11: Mapping Our Environments 15. Assessments 16. Other Books You May Enjoy

Chapter 4: Deep Learning with Neural Networks

This chapter is an introduction to neural networks with Keras. If you have already worked with MNIST or CIFAR-10 image classification datasets, feel free to skip it. But if you have never trained a neural network, this chapter might have some surprises in store for you.

This chapter is quite practical, to give you very quickly something to play with, and we will skip as much theory as reasonably possible and learn how to recognize handwritten numbers (composed of one single digit) with high precision. The theory behind what we do here, and more, will be covered in the next chapter.

We will cover the following topics:

  • Machine learning
  • Neural networks and their parameters
  • Convolutional neural networks
  • Keras, a deep learning framework
  • The MNIST dataset
  • How to build and train a neural network
  • The CIFAR-10 dataset
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