This chapter introduces convolutional neural networks, starting with the convolution operation and moving forward to ensemble layers of convolutional operations, with the aim of learning about filters that operate over datasets. The pooling strategy is then introduced to show how such changes can improve the training and performance of a model. The chapter concludes by showing how to visualize the filters learned.
By the end of this chapter, you will be familiar with the motivation behind convolutional neural networks and will know how the convolution operation works in one and two dimensions. When you finish this chapter, you will know how to implement convolution in layers so as to learn filters through gradient descent. Finally, you will have a chance to use many tools that you learned previously, including dropout and batch normalization, but...
