This intermediate-introductory chapter showed the design of an MLP and the paradigms surrounding its functionality. We covered the theoretical framework behind its elements and we had a full discussion and treatment of the widely known backprop mechanism to perform gradient descent on a loss function. Understanding the backprop algorithm is key for further chapters since some models are designed specifically to overcome some potential difficulties with backprop. You should feel confident that what you have learned about backprop will serve you well in knowing what deep learning is all about. This backprop algorithm, among other things, is what makes deep learning an exciting area. Now, you should be able to understand and design your own MLP with different layers and different neurons. Furthermore, you should feel confident in changing some of its parameters, although we will cover more of this in the further reading.
Chapter 7, Autoencoders, will continue with an architecture...
