A feed-forward neural network (FNN) is a special type of neural network wherein links/connections between neurons do not form a cycle. As such, it is different from other architectures in a neural network that we will get to study later on in this book (recurrent-type neural networks). The FNN is a widely used architecture and it was the first and simplest type of neural network.

In this chapter, we will go through the architecture of a typical ;FNN, and we will be using the TensorFlow library for this. After covering these concepts, we will give a practical example of digit classification. The question of this example is, Given a set of images that contain handwritten digits, how can you classify these images into 10 different classes (0-9)?

The following topics will be covered in this chapter:

• Hidden units and architecture design
• MNIST dataset analysis
• Digit classification - model building and training