In previous class we learn what is MLP? but we listen a term called gradient descent today we study gradient descent. Gradient descent In mathematics, gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the gradient of the function at the current point, because this is the direction of steepest descent. Here it is explained by image
This is the main layer of feature extraction part of the convolution neural networks. Convolution layer They had a filter of shape (x,x), generally x=3 so shape of filter is (3,3). Kernel 2 2 1 3 3 3 3 2 1 Image 2 3 5 3 3 3 (2*2)+(3*2)+(5*1)+(3*3)+(3*3)+(3*3)+(3*3)+(2*2)+(1*1)=56 3 2 1 Every pixel will change by 56 of image
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