## Depthwise Separable Convolution - How does it works?

Convolution is one of the fundamental building block of Deep Neural Network (DNN) but sometimes, it can be extremely expensive to compute since it brings a lots of parameters and we are running risk of overfiting. This is where depthwise separable convolution can be used to reduce the total number of parameters, as a result, speed up convolution. This is a form of factorized convolutions which factorize a standard convolution into a depthwise convolution and a 1 x 1 convolution called a pointwise convolution....