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Showing posts with the label cnn architecture

Importance of pooling layer in CNN

I recently came across a bunch of question regarding pooling in CNN. Will removing the max pooling layer from the CNN architecture effect the accuracy? Is pooling necessary for convolutional neural network? What will happen if we remove pooling layers from the General CNN architecture? Does removing pooling layers from CNN will improve results?   There are many other similar questions like these and this article will answers all those questions. Photo by Thomas Tucker on Unsplash So, firstly if you don't know what pooling is then you can go through this article , it will help you in understanding pooling deeply. And if you just want to know the answers to the above questions then continue with this article. So, we know that pooling helps in reducing the dimension. But why we want to reduce the dimension? The answer is to reduce the computational power required to train the model. If we don't reduce the dimension then our model will take very long or most probably our machine w