Hands-On Transfer Learning with Python

CNNs

CNNs are multilayered neural networks designed specifically for identifying shape patterns with a high degree of invariance to translation, scaling, and rotation in two-dimensional image data. These networks need to be trained in a supervised way. Typically, a labeled set of object classes, such as MNIST or ImageNet, is provided as a training set. The crux of any CNN model is the convolution layer and the subsampling/pooling layer. So, let's understand the operations performed in these layers in detail.