Direct Download The Complete Self-Driving Car Course - Applied Deep Learning (Updated)

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The Complete Self-Driving Car Course - Applied Deep Learning (2019)
WEBRip | English | MP4 + Project files | 1280 x 720 | AVC ~1237 Kbps | 30 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | 18:04:51 | 9.49 GB
Genre: Video Tutorial

Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python

What you'll learn
Learn to apply Computer Vision and Deep Learning techniques to build automotive-related algorithms
Understand, build and train Convolutional Neural Networks with Keras
Simulate a fully functional Self-Driving Car with Convolutional Neural Networks and Computer Vision
Train a Deep Learning Model that can identify between 43 different Traffic Signs
Learn to use essential Computer Vision techniques to identify lane lines on a road
Learn to build and train powerful Neural Networks with Keras
Understand Neural Networks at the most fundamental perceptron-based level
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.
You'll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen.
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 between 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!
No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.
This course also comes with all the source code and friendly support in the Q&A area.
Who this course is for:
Anyone with an interest in Deep Learning and Self Driving Cars
Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence
Entrepreneurs with an interest in working on some of the most cutting edge technologies
All skill levels are welcome!
General
Complete name : 4. Convolutions II.mp4
Format : MPEG-4
Format profile : Base Media
Codec ID : isom (isom/iso2/avc1/mp41)
File size : 79.8 MiB
Duration : 8 min 7 s
Overall bit rate : 1 373 kb/s
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Video
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Format settings, RefFrames : 4 frames
Format settings, GOP : M=4, N=60
Codec ID : avc1
Codec ID/Info : Advanced Video Coding
Duration : 8 min 7 s
Bit rate : 1 237 kb/s
Nominal bit rate : 3 000 kb/s
Width : 1 280 pixels
Height : 720 pixels
Display aspect ratio : 16:9
Frame rate mode : Constant
Frame rate : 30.000 FPS
Color space : YUV
Chroma subsampling : 4:2:0
Bit depth : 8 bits
Scan type : Progressive
Bits/(Pixel*Frame) : 0.045
Stream size : 71.8 MiB (90%)
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Audio
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Codec ID : mp4a-40-2
Duration : 8 min 7 s
Bit rate mode : Constant
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Channel positions : Front: L R
Sampling rate : 44.1 kHz
Frame rate : 43.066 FPS (1024 SPF)
Compression mode : Lossy
Stream size : 7.44 MiB (9%)
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Screenshots







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