Neural Networks and Deep Learning

Undergraduate

presents the theoretical foundations of artificial neural networks. Topics include a mathematical derivation of basic architectures, regularization of neural networks, their stability, generalization abilities and their relation to various areas of mathematics and probability, including hidden Markov chains, stochastic dynamical systems, graph theory and numerical analysis.