Depth tells us the number of filters used. It does not relate to the image depth, nor to the number of hidden layers in the CNN. Each filter or kernel learns different feature maps that are activated in the presence of different image features, such as edges, patterns, and colors.
Applied Deep Learning and Computer Vision for Self-Driving Cars
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Applied Deep Learning and Computer Vision for Self-Driving Cars
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Overview of this book
Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars.
Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.
By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.
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Current Title:
Applied Deep Learning and Computer Vision for Self-Driving Cars
Table of Contents (18 chapters)
Preface
Section 1: Deep Learning Foundation and SDC Basics
Free Chapter
The Foundation of Self-Driving Cars
Dive Deep into Deep Neural Networks
Implementing a Deep Learning Model Using Keras
Section 2: Deep Learning and Computer Vision Techniques for SDC
Computer Vision for Self-Driving Cars
Finding Road Markings Using OpenCV
Improving the Image Classifier with CNN
Road Sign Detection Using Deep Learning
Section 3: Semantic Segmentation for Self-Driving Cars
The Principles and Foundations of Semantic Segmentation
Implementing Semantic Segmentation
Section 4: Advanced Implementations
Behavioral Cloning Using Deep Learning
Vehicle Detection Using OpenCV and Deep Learning
Next Steps
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