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049    INap 
082 04 E VIDEO 
082 04 E VIDEO 
099    Streaming Video O’Reilly for Public Libraries 
100 1  Slim, Rayan,|eauthor. 
245 14 The Complete Self-Driving Car Course - Applied Deep 
       Learning|h[O'Reilly electronic resource] /|cSlim, Rayan.
       |h[O'Reilly electronic resource] 
250    1st edition. 
264  1 |bPackt Publishing,|c2019. 
300    1 online resource (1 video file, approximately 17 hr., 50 
       min.) 
336    two-dimensional moving image|btdi|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
344    digital|2rdatr 
347    video file 
511 0  Presenters, Rayan Slim, Jad Slim, Amer Sharaf, Sarmad 
       Tanveer. 
520    Use deep learning, Computer Vision, and machine learning 
       techniques to build an autonomous car with Python About 
       This Video The transition from a beginner to deep learning
       expert Learn through demonstrations as your instructor 
       completes each task with you No experience required In 
       Detail Self-driving cars have emerged to be one of the 
       most transformative technologies. Fueled by deep learning 
       algorithms, they are rapidly developing 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 one of the only courses that 
       make practical use of deep learning and applies it to 
       building a self-driving car. You'll learn and master deep 
       learning in this fun and exciting course with top 
       instructor Rayan Slim. Having trained thousands of 
       students, Rayan is a highly rated and experienced 
       instructor who follows a learning-by-doing approach. By 
       the end of the course, you will have built a fully 
       functional self-driving car powered 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 do the following
       : Use Computer Vision techniques via OpenCV to identify 
       lane lines for a self-driving car Train a perceptron-based
       neural network to classify between binary classes 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
       Downloading the example code for this course: You can 
       download the example code files for this course on GitHub 
       at the following link: https://github.com/PacktPublishing/
       The-Complete-Self-Driving-Car-Course--Applied-Deep-
       Learning . If you require support please email: 
       customercare@packt.com. 
533    Electronic reproduction.|bBoston, MA :|cSafari.|nAvailable
       via World Wide Web.,|d2019. 
538    Mode of access: World Wide Web. 
542    |fPackt Publishing|g2019 
550    Made available through: Safari, an O'Reilly Media Company.
588    Online resource; Title from title screen (viewed April 4, 
       2019) 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Machine learning. 
650  0 Computer vision. 
650  0 Automated vehicles. 
650  0 Automobiles|xAutomatic control. 
650  0 Automated vehicles|xDesign and construction. 
650  6 Apprentissage automatique. 
650  6 Vision par ordinateur. 
650  6 Véhicules autonomes. 
650  6 Automobiles|xCommande automatique. 
655  4 Electronic videos. 
700 1  Slim, Jad,|eauthor. 
700 1  Sharaf, Amer,|eauthor. 
700 1  Tanveer, Sarmad,|eauthor. 
710 2  Safari, an O'Reilly Media Company. 
776 0  |z1838829415 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/videos/~/9781838829414/?ar|zAvailable
       for O'Reilly for Public Libraries 
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