HOT & NEW | Video: AVC 1280×720 | Audio: AAC 48KHz 2ch | Duration: 12.5 Hours | Lec: 92 | 7.36 GB | Genre: eLearning | Language: English | Sub: EnglishLearn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving carsWhat you’ll learnAutomatically detect lane markings in imagesDetect cars and pedestrians using a trained classifier and with SVMClassify traffic signs using Convolutional Neural NetworksIdentify other vehicles in images using template matchingBuild deep neural networks with Tensorflow and KerasAnalyze and visualize data with Numpy, Pandas, Matplotlib, and SeabornProcess image data using OpenCVCalibrate cameras in Python, correcting for distortionSharpen and blur images with convolutionDetect edges in images with Sobel, Laplace, and CannyTransform images through translation, rotation, resizing, and perspective transformExtract image features with HOGDetect object corners with HarrisClassify data with machine learning techniques including regression, decision trees, Naive Bayes, and SVMClassify data with artificial neural networks and deep learningRequirementsWindows, Mac, or Linux PC with at least 3GB free disk space.
Some prior experience in programming.
DescriptionAutonomous Cars: Computer Vision and Deep LearningThe 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:OpenCVDeep Learning and Artificial Neural NetworksConvolutional Neural NetworksTemplate matchingHOG feature extractionSIFT, SURF, FAST, and ORBTensorflow and KerasLinear regression and logistic regressionDecision TreesSupport Vector MachinesNaive BayesWho is the target audience?Software engineers interested in learning the algorithms that power self-driving cars.
