The course aims at teaching the required skills to
use deep learning methods on applied problems.
It will show how to design and train a deep
neural network for a given task, and the sufficient
theoretical basis to go beyond the topics directly
seen in the course. The planned content of
the course:
• What is deep learning, introduction to tensors.
• Basic machine-learning, empirical risk
minimization, simple embeddings.
• Linear separability, multi-layer perceptrons,
back-prop.
• Generalized networks, autograd, batch processing,
convolutional networks.
• Initialization, optimization, and regularization.
Drop-out, activation normalization, skip
connections.
• Deep models for Computer Vision.
• Analysis of deep models.
• Auto-encoders, embeddings, and generative models.
• Deep learning for sequences - Recurrent neural
networks (RNNs); vanishing and exploding
gradients; Long Short-Term Memory (LSTM);
deep RNNs; bidirectional RNNs; combination of
CNNs with RNNs - pytorch tensors, deep learning
modules, and internals.
Outcome: Not Provided
