In this lecture, I will cover the basic concepts behind feedforward neural networks. The talk will be split into 2 parts. In the first part, I'll cover forward propagation and backpropagation in neural networks. Specifically, I'll discuss the parameterization of feedforward nets, the most common types of units, the capacity of neural networks and how to compute the gradients of the training loss for classification with neural networks. In the second part, I'll discuss the final components necessary to train neural networks by gradient descent and then discuss the more recent ideas that are now commonly used for training deep neural networks. I will thus present different variants of gradient descent algorithms, dropout, batch normalization and unsupervised pretraining.