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A simple explanation to filters, stride and padding in convolution neural networks

We know that CNN use filters to extract feature from the image. This filter moves across the image starting from top left and moving towards right. Once it reaches at the horizontal end of the image, it moves one step vertically downwards and then start moving from left to right. It repeats this process until it reaches at the lower right corner of the image. Photo by Birger Strahl Now lets see how these filters extract the features from the image, but before that lets understand what filters actually are? What are Filters? Filters are nothing but a matrix of specific number which are when multiplied by an image gives a particular feature map. For example, in the below image when multiplied by a “Right sobel” filter gives an output feature map with vertical lines detected in it. Right sobel filter on a 2d image Different filters have different specific values to detect different features. Different filter and their values Now imagine the below image as a image matrix and we pass a

Convolutional Neural Networks are easier than you think

  Photo by Kristopher Roller on Unsplash In our last article on neural networks , we learned how neural networks work. In this article we will look into another type of a neural network used in deep learning, Convolution Neural Network  also known as ConvNet/CNN. What is Convolutional neural network? A convolutional neural network is a Deep Learning  algorithm  which is designed for working with two dimensional images. It applies a filter to the input image to extract the features from the input. The same filter is applied multiple times to the input to generate a feature map which indicates the strength of the detected features. The pre processing required in a ConvNet is much lower as compared to other classification algorithms . Why CNN? Why not use simple Neural Network? To answer this question first we need to understand how convNet works. How convolutional neural networks works? Before understanding the working of convolutional neural networks let us understand how w