Convolutional Neural Networks (CNN) explained step by step




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Convolutional Neural Networks are a bit different than the standard neural networks. First of all, the layers are organized in 3 dimensions: width, height, and depth. Further, the neurons in one layer do not connect to all the neurons in the next layer but only to a small region of it. Lastly, the final output will be reduced to a single vector of probability scores, organized along the depth dimension.

Conventionally, the first ConvLayer is responsible for capturing the Low-Level features such as edges, color, gradient orientation, etc.
With added layers, the architecture adapts to the High-Level features as well, giving us a network that has the wholesome understanding of images in the data-set, similar to how we would.

Text version tutorial: https://pylessons.com/CNN-tutorial-introduction/
CNN full video playlist: https://www.youtube.com/playlist?list=PLbMO9c_jUD47xb1krmzQ9nm6X_KSR5Jb1

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Comment List

  • Python Lessons
    December 18, 2020

    This is short introduction to CNN theory before doing some cool stuff in practice!

  • Python Lessons
    December 18, 2020

    How to define parameter 20×1 of fully connected layer ReLU Activation

  • Python Lessons
    December 18, 2020

    Well done !

  • Python Lessons
    December 18, 2020

    I have a question. Let At first convolution layer if we apply 32 filters on a gray scale image then output of first layer would be 32 matrixes or say 32 filtered images. Then at second layer if we are applying 64 filters then does it mean that we are applying 64 different filters over each of 32 filtered images???? And output of second layer would be 64*32=2048 filtered images???. Plz let it clear if anyone can

  • Python Lessons
    December 18, 2020

    What is the meaning of negative values in the feature map?

  • Python Lessons
    December 18, 2020

    How convolution filters value are chosen? How you are finding the error of fully connected network?

  • Python Lessons
    December 18, 2020

    How are the filters set? Are they statically set when the network is built? Or do they keep dynamically changing during back propogation? I am curious how the filter values are chosen, and whether those filter values ever change as the network learns.

  • Python Lessons
    December 18, 2020

    Thanks

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