Why convolutions ?  In this post we will talk about why  convolutions or convolutional neural networks work so well in a computer vision. Convolutions are very useful when we include them in our neural networks. There are two main advantages of \(Convolutional \) layers over \(Fully\enspace connected\) layers: parameter sharing and sparsity of connections. We can illustrate an example. Let’s say that we have a \(32\times32\times3\) dimensional image. This actually comes from the example from the previous…
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Pooling layers Apart from convolutional layers, \(ConvNets \) often use pooling layers to reduce the image size. Hence, this layer speeds up the computation and this also makes some of the features they detect a bit more robust. Let’s go through an example of pooling, and then we’ll talk about why we might want to apply them. There are two types of pooling: \(Max \enspace pooling \) \(Average \enspace pooling \) \(Max \enspace pooling \) Suppose…
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A simple Convolutional Neural Network  – A ConvNet In the last post we saw the building blocks of a single convolutional layer in a \(ConvNet \). Now let’s go through a concrete example of a simple convolutional neural network. Let’s say we have an image and we want to do an image classification or image recognition. We want to take an image \(X \)  as input and decide if this is a cat or not a…
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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…
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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…
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