A. Formal Neural Network Notation
To keep discussion of artificial neurons as straightforward as possible, in this book we used a shorthand notation to identify them within a network. In this appendix, we lay out a more widely used formal notation, which may be of interest if you’d like to:
Possess a more precise manner for describing neurons
Follow closely the backpropagation technique covered in Appendix B
Taking a look back at Figure 7.1, the neural network has a total of four layers. The first is the input layer, which can be thought of as a collection of starting blocks for each data point to enter the network. In the case of the MNIST models, for example, there are 784 such starting blocks, representing each of the pixels in a 28×28–pixel ...