Introduction to Deep Learning: Introduction, Shallow Learning, Deep Learning, Why to use Deep Learning, How Deep Learning Works,Deep Learning Challenges,. How Learning Differs from Pure Optimization, Challenges in Neural Network Optimization.

Basics of Supervised Deep Learning: Introduction, Convolution Neural Network, Evolution of Convolution Neural Network, Architecture of CNN, Convolution Operation.

Training Supervised Deep Learning Networks: Training Convolution Neural Networks, Gradient Descent-Based Optimization Techniques, Challenges in Training Deep Networks.

Supervised Deep Learning Architectures: LetNet-5,AlexNet.

Recurrent and Recursive Neural Networks: Unfolding Computational Graphs, Recurrent Neural Network, Bidirectional RNNs, Deep Recurrent Networks, Recursive Neural Networks, The Long Short-Term Memory.Gated RNNs.

Deep Reinforcement Learning: Introduction, Stateless Algorithms: Multi-Armed Bandits, The Basic Framework of Reinforcement Learning, case studies.