Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques
Product ID: 297246880
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Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV Key Features Build and train powerful neural network models to build an autonomous car Implement computer vision, deep learning, and AI techniques to create automotive algorithms Overcome the challenges faced while automating different aspects of driving using modern Python libraries and architectures Book Description 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. What you will learn Implement deep neural network from scratch using the Keras library Understand the importance of deep learning in self-driving cars Get to grips with feature extraction techniques in image processing using the OpenCV library Design a software pipeline that detects lane lines in videos Implement a convolutional neural network (CNN) image classifier for traffic signal signs Train and test neural networks for behavioral cloning by driving a car in a virtual simulator Discover various state-of-the-art semantic segmentation and object detection architectures Who this book is 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. Review: A well organized guide to go from noob to novice in computer vision and DL - This book is for anyone who is looking to get a good handle on Computer vision and get started with Deep learning. I have benefited a lot from the teach and apply approach used in this book. The chapters are organized, and their order makes sense. The codes are self-explanatory, and enough help provided for you to work through it. The only thing I can point my finger at is there should be a notation guide for people who are not at all familiar with deep learning. Apart from that, this is an excellent piece of work by the authors, and I recommend it strongly to get your hands dirty with Computer vision techniques and deep learning, the building blocks to get into the self-driving field Review: This book gives you just basic and idea about SDCs (Not for SDCs researcher or developer) - I was interested to read this book because I am working on Autonomous Vehicle but after reading this whole book(PDF) writing this review. This book not for SDCs researcher or developer, who wants to learn deep learning and computer vision in details. The book writer did not cover custom training for ENet( Semantic segmentation model) and YOLOv3 (Object detection model) just used a pre-trained model and did inference on it. This is not good for a book writer. If you want to get a little flavor for SDCs then you should go. Note: This is my review, your review may be different.
| Best Sellers Rank | #707,382 in Books ( See Top 100 in Books ) #1,997 in Artificial Intelligence #7,702 in Computer Science Books |
| Customer Reviews | 3.9 out of 5 stars 33 Reviews |
A**R
A well organized guide to go from noob to novice in computer vision and DL
This book is for anyone who is looking to get a good handle on Computer vision and get started with Deep learning. I have benefited a lot from the teach and apply approach used in this book. The chapters are organized, and their order makes sense. The codes are self-explanatory, and enough help provided for you to work through it. The only thing I can point my finger at is there should be a notation guide for people who are not at all familiar with deep learning. Apart from that, this is an excellent piece of work by the authors, and I recommend it strongly to get your hands dirty with Computer vision techniques and deep learning, the building blocks to get into the self-driving field
A**Z
This book gives you just basic and idea about SDCs (Not for SDCs researcher or developer)
I was interested to read this book because I am working on Autonomous Vehicle but after reading this whole book(PDF) writing this review. This book not for SDCs researcher or developer, who wants to learn deep learning and computer vision in details. The book writer did not cover custom training for ENet( Semantic segmentation model) and YOLOv3 (Object detection model) just used a pre-trained model and did inference on it. This is not good for a book writer. If you want to get a little flavor for SDCs then you should go. Note: This is my review, your review may be different.
B**E
Good book for a complicated, experiential Learning domain.
It is a good book dealing with computer vision and enables the computation models to see patterns and followed by the interpretation. The book is filled with various examples of computation and scientific details. This must have based on the research and finding of more than 3-4 decades of research by scientists. The advanced mathematical formula and algorithm may appear tricky to grasp at first. And It may be overwhelming to a beginner. Before the closing of every chapter, the summaries are short and sweet to grasp the concept. The books provide illustrative examples of image detection along with code samples. In terms of commercial success of automation vehicles and the level of automation, the book shed some light and generate interest. The books cover findings on the computer vision which is truly scientific and based on experiential learning so that machines can navigate themselves in an unknown world. Overall, it provides a good learning experience for a highly technical, ambitious and sophisticated concept of autonomous driving.
P**I
Great Book for Self Driving Car and Computer Vision enthusiastic
This is a great book for Self Driving Car and Computer Vision enthusiastic. Such a great learning experience, simple explanation of each topic. This book covers Self-Driving Cars with example of few deployments, Computer Vision using openCV, Convolution Network Network, Semantic Segmentation ,Object Detection Algorithm like YOLO. All topics are explained with practical examples using openCV and Keras. The book covers multiple project implementations like lane lines finding, Traffic Sign detection, prototype of Self Driving Cars using behavior cloning, implementation of projects on ENET and YOLO. Recommend the book if you want to learn deep learning for computer vision using Self Driving cars applications.
P**R
Nice book to learn AI
I have read this book and believe me its a great book for any beginner. The use cases are so well written that users can easily understand. I would definitely recommend this book to anyone who want to start their journey into AI world. I would like to thank Sumit for providing such a great book and like to thank him for helping all the budding engineers to get on speed on the latest technologies.
V**.
Good and simply written book
This is a really good book which explains one of the most complicated application of machine learning in simple words and concepts. Every concept is explained in a simple way which make it easy to follow. Author also included some programming that too in a simple way.
K**R
A book with clarity.
It has a simple, to the point, understanding approach with sequential implementation steps and coding. All related detailing is rightly referenced. For a beginner it’s an absolutely efficient manual.
Y**A
Perfect comprehensive coverage
Starting from the basics, it covers each and every aspect of using cnn in self driving cars along with the practical guidance.
J**S
Too sketchy
The book is nicely structured and provides a good overview in theory, but the actual content and chapter semantics is too sketchy and unprecise to provide the understanding needed. I used it as a structured reference for what to look up on internet to get a real understanding.
J**A
Très intéressant
Très intéressant
A**M
It is good
I am happy with that
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