Lecture
This lecture covers the aims and practical questions related to deep learning, including the choice of neuron types, network architecture, and optimization methods. It reviews artificial neural networks, multilayer perceptron, and deep neural networks, discussing the choice of neuron models and activation functions. The lecture also delves into the initialization of weights in backpropagation and addresses the importance of initialization or normalization in backpropagation. Additionally, it explores single-layer networks, the problem of overfitting, and models for regularization of deep networks.