Deep Learning and Computer Vision in Python 2018-12 – Download

explanation

Self-Driving Cars: Deep Learning and Computer Vision in Python, Course Name, OpenCV, Keras, Diagnostics, Tracking and Objects, Traffic Sign Classification, Driving for the Self-Driving Car Industry. The automotive industry and propellants are experiencing a paradigm shift and a new industry. Cars that drove humans, self-driving cars with enhanced artificial intelligence. Autonomous vehicle solutions are safe, efficient and affordable, and will redefine human transformation. Forecasts show that by 2035, self-driving cars will reach 500,000 people, while creating economic opportunities of up to $1 trillion. The goal of this course is to provide knowledge that one of them is the key to designing and developing the autonomous machine industry.

In Why Self-Driving Cars: Deep Learning and Computer Vision in Python you will learn:

Auto-detect will show you the path to the photo.
Category a Vehicle and passerby diagnosis using training data and SVM
Traffic sign classification, driving using neural networks
Detect other vehicles in images using compliance templates.
Building neural networks using Tensorflow and Keras
Data analysis and illustration using Numpy, Pandas, Matplotlib, and Seaborn
Image processing using OpenCV
I calibrated the camera with Python to correct for deviations.
Use convolutions to sharpen and blur images.
Edge detection in images using Sobel, Laplace, and Canny
Change the appearance of images by transferring, rotating, etc. Change the size and shape of the perspective.
Extract images using HOG
Image classification using neural network dummy and deep learning
Data classification using machine learning technology includes regression analysis. Decision trees, etc. Naive Bayes and SVM

Specification 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 45 minutes