One layer of a Convolutional Neural Network We will now present how to make one convolutional layer within our network. Let’s go through the example. We’ve seen in the previous post how to take a 3D volume and convolve it with two different filters in order to get two different \(4 \times 4 \) outputs. An example of a convolution with two different filters Convolving with the first filter gives us one \(4 \times 4…
Read more
How do we make convolutions on RGB images? We’ve seen how convolutions over 2D images work. Now, let’s see how we can implement convolutions over not just 2D images, but over three-dimensional volumes. For example, if we want to detect features, not just in a grayscale image, but in an RGB image. 2D (or a grayscale) image and 3D (or a RGB) image Instead of a \(6 \times 6 \) image, an RGB image could…
Read more
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…
Read more
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…
Read more
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…
Read more
