Applied Deep Learning and Computer Vision for Self-Driving Cars
This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.
Offered by
Difficulty Level
Intermediate
Completion Time
11h4m
Language
English
About Book
Who Is This Book For?
If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.
Applied Deep Learning and Computer Vision for Self-Driving Cars
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h4m total length
The Foundation of Self-Driving Cars
Dive Deep into Deep Neural Networks
Implementing a Deep Learning Model using Keras
Computer Vision for Self-Driving Cars
Finding Road Markings using OpenCV
Improving the Image Classifier with CNN
Road Sign Detection using Deep Learning
The Principles and Foundations of Semantic Segmentation
Implementation of Semantic Segmentation
Behavior Cloning using Deep Learning
Vehicle Detection using OpenCV and Deep Learning
Next Steps
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