https://towardsdatascience.com/mobilenetv2-inverted-residuals-and-linear-bottlenecks-8a4362f4ffd5 > In April 2017 a group of researchers from Google published a paper which introduced a neural network architecture that was optimized for mobile devices. They strived for a model that delivered high accuracy while keeping the parameters and mathematical operations as low as possible. This was much needed in order to bring deep neural networks to smartphones. > The architecture dubbed MobileNet revolves around the idea of using depthwise separable convolutions, which consist of a depthwise and a pointwise convolution after one another. If you’re a little fuzzy on the details of this operation feel free to check out my other article that explains this concept in detail. MobileNetV2 extends its predecessor with 2 main ideas.