Convolutional Neural Networks (CNN) explained step by step
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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