Autonomous Cars: Deep Learning and Computer Vision in Python [Video]

More Information
Learn
  • Identify lane markings in images and detect cars and pedestrians using a trained classifier and SVM
  • Classify traffic signs using CNNs
  • Analyze and visualize data with NumPy, Pandas, Matplotlib, and Seaborn
  • Process image data using OpenCV
  • Sharpen and blur images with convolution and detect edges in images with Sobel, Laplace, and Canny
  • Transform images through translation, rotation, resizing, and perspective transform
  • Extract image features with HOG and detects object corners with Harris
  • Classify data with artificial neural networks and deep learning
About

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.

Features
  • Learn complex topics such as artificial intelligence (AI) and machine learning through a systematic and helpful teaching style
  • Build deep neural networks with TensorFlow and Keras
  • Classify data with machine learning techniques such as regression, decision trees, Naive Bayes, and SVM
Course Length 12 hours 14 minutes
ISBN 9781838988463
Date Of Publication 31 May 2019

Authors

Stemplicity School Online Inc.

Dr. Ryan Ahmed - Ph.D., MBA Ryan Ahmed is a best-selling instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Engineering from McMaster* University, with a focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with a focus on Artificial Intelligence (AI) and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Engineering, Science, Technology, and Mathematics to over 35,000+ students globally. He has over 15 published journal and conference research papers on AI, Machine learning and EV controls. He is the recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. Mitchell Bouchard - MFA Candidate, B.S, Host at Red Cape Learning. Mitch is a Canadian filmmaker from Harrow Ontario, Canada. In 2016 he graduated from Dakota State University with a B.S, in Computer Graphics specializing in Film and Cinematic Arts. Currently, Mitch operates as the Chairman of Red Cape Studios, Inc. where he continues his passion for filmmaking. He is also the Host of Red Cape Learning and Produces / Directs content for Red Cape Films. Mitch is currently working as a Graduate Assistant and is an MFA Candidate at the University of Windsor. Winning several awards at Dakota State University such as "1st Place BeadleMania", "Winner College 10th Anniversary Dordt Film Festival" as well as "Outstanding Artist Award College of Arts and Sciences". Mitch has been Featured on CBC's "Windsors Shorts" Tv Show and was also the Producer/Director for TEDX Windsor, featuring speakers from across the Country.