In this chapter we'll introduce feedforward networks as classifiers, and also apply them to the simple task of language modeling: assigning probabilities to word sequences and predicting upcoming words. In subsequent chapters we'll introduce many other aspects of neural models, such as recurrent neural networks and the Transformer (Chapter 9), contextual embeddings like BERT (Chapter 11), and encoder-decoder models and attention (Chapter 10).