Strided convolution A strided convolution is another basic building block of convolution that is used in Convolutional Neural Networks. Let’s say we want to convolve this \(7 \times 7 \) image with this \(3 \times 3 \) filter, except, that instead of doing it the usual way, we’re going to do it with a stride of \(2 \). Convolutions with a stride of two This means that we take the element-wise product as usual in…
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Padding In order to build deep neural networks, one modification to the basic convolutional operation that we have to use is padding. Let’s see how it works. What we saw in earlier posts is that if we take a \(6 \times 6 \) image and convolve it with a \(3 \times 3 \) filter, we end up with a \(4 \times 4 \) output (or with a \(4 \times 4 \) matrix), and that’s because…
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More on Edge Detection We’ve seen in the previous post how the convolution operation allows us to implement a vertical edge detector. In this post we will learn: the difference between positive and negative edges and what is the difference between light to dark versus dark to light edge transitions types of filters ( detectors ) how an algorithm can learn detector’s parameters (coefficients) A vertical edge detector Let’s have a look at this \(6…
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Convolutional operation The operation of the convolution is one of the foundations of the convolutional neural networks. From the Latin word , “to convolve” means to roll together. For mathematical purposes, a convolution is the integral measuring how much two functions overlap as one passes over the other. Think of a convolution as a way of mixing two functions by multiplying them. Using the edge detection as a starting point, we will see how the convolution…
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In this post we will see how to build a shallow Neural Network in Python. A Shallow Neural Network First we will import all libraries that we will use it this code. Then we will define our datasets. Those are two linearly non-separable datasets. To getreate them we can use either make_circles or make_moons function from Sci-kit learn. We need to define activation functions that we will use in our code. Following function initializes parameters…
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