title: Depthwise separable Convolution
body: <p>I am new to Deep Learning and I recently came across Depth Wise Separable Convolutions. They significantly reduce the computation required to process the data and need only like 10% of standard convolution step computation. </p>
  
  <p>I am curious as to what is the intuition behind doing this? We do achieve greater speed by reducing number of parameters and doing less computation, but is there a trade-off in performance?</p>
  
  <p>Also, is it only used for some specific use cases like images, etc or can it be applied to all forms of data?</p>