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

    Posted By: ParRus
    The Complete Self-Driving Car Course - Applied Deep Learning (Updated)

    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!

    also You can find my other useful: programming-posts

    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
    Writing application : Lavf58.12.100

    Video
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    Format : AVC
    Format/Info : Advanced Video Codec
    Format profile : Main@L3.1
    Format settings : CABAC / 4 Ref Frames
    Format settings, CABAC : Yes
    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%)
    Writing library : x264 core 148
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    Audio
    ID : 2
    Format : AAC
    Format/Info : Advanced Audio Codec
    Format profile : LC
    Codec ID : mp4a-40-2
    Duration : 8 min 7 s
    Bit rate mode : Constant
    Bit rate : 128 kb/s
    Channel(s) : 2 channels
    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%)
    Default : Yes
    Alternate group : 1

    Screenshots

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

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

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

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

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

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