The document discusses the first half of deep learning. It covers input layers, hidden layers, and output layers. It also discusses activation functions like step functions, sigmoid functions, tanh functions, ReLU, and softmax functions. Gradient descent methods for optimizing neural networks like momentum, AdaGrad, RMSProp, and Adam are introduced. Error backpropagation for efficiently calculating gradients is also covered.