Introduction to deep learning. Historical perspective. Applications of deep learning.

Training deep neural networks. Feedforward neural networks. Stochastic Gradient Descent. Backpropagation. Activation and loss functions. Regularization, initialization, normalization. Parameter updates.

Convolutional Neural Networks. Convolution layer. Pooling layer. CNN architectures. Image classification. Image segmentation. Visualizing and interpreting CNNs.

Recurrent Neural Networks. Backpropagation through time. RNN. Long Short-Term Memory. Gated Recurrent Units. Language model and sequence generation. Image captioning.

Beyond supervised learning. Autoencoders. Variational Autoencoders. Generative Adversarial Networks. Deep Reinforcement Learning.

Applications of deep learning. Computer vision. Speech recognition. Natural language processing.