Autonomous Cars: Deep Learning and Computer Vision in Python

Autonomous Cars: Deep Learning and Computer Vision in PythonLearn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars

Created by Sundog Education by Frank Kane, Frank Kane, Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard

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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