Deep Learning Fundamentals

Fundamental principles and techniques in deep and reinforcement learning, convolutional neural networks, recurrent and recursive neural networks, backpropagation algorithms, regularization and optimization techniques for training such networks, dynamic programming, Monte Carlo, and temporal difference, function approximation reinforcement learning algorithms, applications of deep and reinforcement learning, and active research topics in deep and reinforcement learning areas.