Summary
Feedforward networks are a basic and essential class of network. This chapter has helped us study the building blocks of neural networks, and will help illuminate network topics going forward.
Feedforward neural networks are best represented as directed graphs; information flows through in one direction and is transformed by matrix multiplications and activation functions. Training cycles in ANNs are broken into epochs, each of which contains a forward pass and a backwards pass. On the forward pass, information flows from the input layer, is transformed via its connections with the output layers and their activation functions, and is put through an output layer function that renders the output in the form we want it; probabilities, ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access