HOT & NEW | Video: AVC 1280x720 | Audio: AAC 48KHz 2ch | Duration: 12.5 Hours | Lec: 92 | 7.36 GB | Genre: eLearning | Language: English | Sub: English

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars

What you'll learn

Automatically detect lane markings in images

Detect cars and pedestrians using a trained classifier and with SVM

Classify traffic signs using Convolutional Neural Networks

Identify other vehicles in images using template matching

Build deep neural networks with Tensorflow and Keras

Analyze and visualize data with Numpy, Pandas, Matplotlib, and Seaborn

Process image data using OpenCV

Calibrate cameras in Python, correcting for distortion

Sharpen and blur images with convolution

Detect edges in images with Sobel, Laplace, and Canny

Transform images through translation, rotation, resizing, and perspective transform

Extract image features with HOG

Detect object corners with Harris

Classify data with machine learning techniques including regression, decision trees, Naive Bayes, and SVM

Classify data with artificial neural networks and deep learning

Requirements

Windows, Mac, or Linux PC with at least 3GB free disk space.

Some prior experience in programming.

Description

Autonomous Cars: Computer Vision and Deep Learning

The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles into self-driving, artificial intelligence-powered vehicles. Self-driving vehicles offer a safe, efficient, and cost effective solution that will dramatically redefine the future of human mobility. Self-driving cars are expected to save over half a million lives and generate enormous economic opportunities in excess of $1 trillion dollars by 2035. The automotive industry is on a billion-dollar quest to deploy the most technologically advanced vehicles on the road.

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.

The purpose of this course is to provide students with knowledge of key aspects of design and development of self-driving vehicles. The course provides students with practical experience in various self-driving vehicles concepts such as machine learning and computer vision. Concepts such as lane detection, traffic sign classification, vehicle/object detection, artificial intelligence, and deep learning will be presented. The course is targeted towards students wanting to gain a fundamental understanding of self-driving vehicles control. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this self-driving car course will master driverless car technologies that are going to reshape the future of transportation.

Tools and algorithms we'll cover include:

OpenCV

Deep Learning and Artificial Neural Networks

Convolutional Neural Networks

Template matching

HOG feature extraction

SIFT, SURF, FAST, and ORB

Tensorflow and Keras

Linear regression and logistic regression

Decision Trees

Support Vector Machines

Naive Bayes

Who is the target audience?

Software engineers interested in learning the algorithms that power self-driving cars.