Feed-Forward Neural Networks

In this chapter, we will implement Feed-Forward Neural Networks (FNN) and discuss the building blocks for deep learning:

• Understanding the perceptron
• Implementing a single-layer neural network
• Building a multi-layer neural network
• Getting started with activation functions
• Hidden layers and hidden units
• Implementing an autoencoder
• Tuning the loss function
• Experimenting with different optimizers
• Improving generalization with regularization
• Adding dropout to prevent overfitting