This intermediate chapter showed how to create CNNs. You learned about the convolution operation, which is the fundamental concept behind them. You also learned how to create convolutional layers and aggregated pooling strategies. You designed a network to learn filters to recognize objects based on CIFAR-10 and learned how to display the learned filters.
At this point, you should feel confident explaining the motivation behind convolutional neural networks rooted in computer vision and signal processing. You should feel comfortable coding the convolution operation in one and two dimensions using NumPy, SciPy, and Keras/TensorFlow. Furthermore, you should feel confident implementing convolution operations in layers and learning filters through gradient descent techniques. If you are asked to show what the network has learned, you should feel prepared to implement a simple visualization method to display the filters learned.
CNNs are great at encoding highly correlated spatial...
