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In fact, you can solve your linear regression problem by different methods: normal equations (the way you mentioned), QR/SVD decomposition or an iterative method to minimize the error directly (like what the gradient descent method is doing). Note that the other methods give you the exact solution (ignoring the round-off error) while, as the GD method is ...


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I'd like to recommend this limpid article: CS231n Convolutional Neural Networks for Visual Recognition, and let me compare the (simplified) vanilla network with the (simplified) residual network as follows. Here is a diagram I borrowed from that page: where the green numbers above the lines are indicating the forward pass, and the red numbers the ...


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