In this video, we will understand what is Max Pooling in Convolutional Neural Network and why do we use it.
Max Pooling in Convolutional Neural Network is an important part of the CNN Architecture, where it is used to reduce the dimension of the image and enhance the features of the image as well.
Reducing the dimension of the image reduces the computational cost and the number of operations required to process the image. And enhancing the features helps our model to detect and understand features easily.
Max Pooling works by using a filter as a sliding window and extracting the maximum value from that window.
Max Pooling in Convolutional Neural Network can highly increase the performance of the Convolutional Neural Network.
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Timestamp:
0:00 Intro
1:01 What is Max Pooling in Convolutional Neural Network
2:18 Why Max Pooling?
6:08 Average Pooling
6:25 Summary
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