Deep Learning

An overview course on basic neural network models and their application to image, text, and sound processing tasks.

Feedforward networks. Matrix-vector differentiation. Automatic differentiation.
Neural network optimization. DropOut. PyTorch.
Initialization of neural networks. Types of normalization, convolutional neural networks, implementation of convolutional networks in PyTorch.
Recurrent neural networks. Machine translation task. Attention mechanism. Transformer model.
Object detection.
Semantic segmentation. Instance/Panoptic segmentation.
Reinforcement learning. Markov’s decision making process. Bellman equations.
Policy iteration, Value iteration algorithms. Q-learning. DQN model.
Algorithm Reinforce, A2C algorithm. Multi-armed bandits.
Generative models. Variational autoencoder model.
Autoregressive models: PixelCNN, PixelRNN. Reparametrization trick.
Generative adversarial networks and their modifications. Image quality metrics.

5 practical tasks and 1 lab work. Each task is evaluated from 10 points. The exam is evaluated from 10 points.

0.7 * semester points + 0.3 * exam points.

Machine learning, statistics, optimization methods.