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After reading this blog series you able to solve any deep learning problem

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Gradient Descent

 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
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Convolution layer

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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Flatten

 Flatten can convert 2D image to a 1D tensor Example-Image - [ 3 1 5   1 1 5] Flatten- [ 3 1 5 1 1 5 ]
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