Packt – Autonomous Cars Deep Learning and Computer Vision in Python-JGTiSO
English | Size: 3.20 GB
Category: Tutorial

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
Analyse 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
About
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. [Read more…]