How to train a neural network for training regression model?

In previous class we discus about to train multiclass classification to convert it to regression model we have to just change last neuron to single perceptron

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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

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

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 ]

Deep Learning

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