Deep Learning and Computer Vision in Python 2018-12

Description

Autonomous Cars: Deep Learning and Computer Vision in Python, Training Course Name, OpenCV, Keras, Diagnostics, Tracking and Objects and Road Sign Classification, Driving for the Autonomous Car Industry. The automotive industry, the fuel industry, is experiencing a paradigm shift, a new industry: the automobile, the shared human-driven autonomous cars augmented by artificial intelligence. Autonomous car is a solution that is safe, efficient and affordable and will redefine transportation for people. The forecast assumes that half a million people will keep autonomous cars by 2035, on the other hand, the economic opportunities will impose a large volume on them, up to $ 1 trillion. The aim of this course is to impart knowledge that is one of the keys to the design and development of the autonomous machine industry.

What’s behind autonomous cars: Deep learning and computer vision in Python you can find out:

Automatic detection marks the path in the image
Vehicle and pedestrian diagnosis using Category A and SVM training data
Classification of traffic signs, driving with the use of convolutional neural networks
Detect other vehicles in the image using the compliance template
Build neural networks using Tensorflow and Keras
Analyzing and plotting data using Numpy, Pandas, Matplotlib and Seaborn
Image processing with OpenCV
Calibrated camera in Python to correct for deviations
Sharpen and blur images using convolution
Edge detection in images using Sobel, Laplace and Canny
Change the shape of the images by transferring, rotating, etc. Change the size and shape of the perspective
Extract images with HOG
Classifying images using neural networks, dummy and deep learning
Classification of data using machine learning techniques includes regression etc. Decision trees etc. Naive Bayes and SVM

Specifications Volume:

Publisher: Udemy
Teacher: Sundog Education by Frank Kane, Frank Kane, Ryan AhmedMitchell H
Language: English
Training level: basic to advanced
Number of courses: 93
Duration: 12 hours and 45 minutes