The Complete Self-Driving Car Course - Applied Deep Learning .

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The Complete Self-Driving Car Course - Applied Deep Learning .

The Complete Self-Driving Car Course - Applied Deep Learning
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 17 Hours 40M | 3.99 GB
Genre: eLearning | Language: English


Self-driving cars have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today. Learn & Master Deep Learning in this fun and exciting course with top instructor Rayan Slim. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course. By the end of the course, you will have built a fully functional self-driving car fuelled entirely by Deep Learning. This powerful simulation will impress even the most senior developers and ensure you have hands on skills in neural networks that you can bring to any project or company.

This course will show you how to:

Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car.
Learn to train a Perceptron-based Neural Network to classify between binary classes.
Learn to train Convolutional Neural Networks to identify various traffic signs.
Train Deep Neural Networks to fit complex datasets.
Master Keras, a power Neural Network library written in Python.
Build and train a fully functional self-driving car to drive on its own!
All the code and supporting files for this course are available at:
https://github.com/PacktPublishing/The-Complete-Self-Driving-Car-Course--Applied-Deep-Learning


The Complete Self-Driving Car Course - Applied Deep Learning .


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