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.