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How Pooling layer helps in reducing dimension in convolutional neural networks?

One of the most important layer in a CNN architecture is Pooling layer. In this article we will understand what is pooling layer? what does pooling layer do? and how pooling layer works? we will also look at different types of pooling such as max pooling, average pooling and global average pooling. Photo by Andras Kerekes What is Pooling layer and what does pooling layer do?  In simple words Pooling is used for dimensionality reduction in CNN. Why dimensionality reduction? For decreasing the computational power required to process the data. But pooling is not just for reducing the dimension only, it also helps in extracting the dominant features like edges in the image. How pooling layer works? So, now we know that pooling is used for dimensionality reduction but how pooling reduces dimension? Pooling works similar to filters. Consider the below image where we are using a filter of size 2 X 2. In case of filters, we used to multiply filter values to the input element wise and c

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