Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars.
About This Video
The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles to self-driving, artificial intelligence-powered vehicles. As the world advances towards a driverless future, the need for experienced engineers and researchers in this emerging new field has never been more crucial. This course will guide you through the key design and development aspects of self-driving vehicles.
You’ll be exploring OpenCV, deep learning, and artificial neural networks and their role in the development of autonomous cars. The book will even guide you through classifying traffic signs with convolutional neural networks (CNNs). In addition to this, you’ll use template matching to identify other vehicles in images, along with understanding how to apply HOG for extracting image features. As you progress, you’ll gain insights into feature detectors, including SIFT, SURF, FAST, and ORB. Next, you’ll get up to speed with building neural networks using Keras and TensorFlow, and later focus on linear regression and logistic regression. Toward the concluding part, you’ll explore machine learning techniques such as decision trees and Naive Bayes for classifying data, in addition to understanding the Support Vector Machine (SVM) method.
By the end of this course, you’ll be well-versed with key concepts related to the design and development of self-driving vehicles.
Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Autonomous-Cars-Deep-Learning-and-Computer-Vision-in-Python. If you require support please email: email@example.com