Lectures

Zoom link for lectures

The lecture presentations and the accompanying material for each lecture is posted here.

Lecture 2: Neural Networks

Linear and multilayer Perceptron, loss functions, activation functions, pooling, weight sharing, convolutional layers, gradient descent, backpropagation.

Lecture 4: Optimization

Approximation, estimation and optimization errors, regularization, loss surface curvature, descent-based optimization methods, second-order methods.

Lecture 7: Sequence Models

RNN model, input-output sequences relationships, non-sequential input, layered RNN, backpropagation through time, word embeddings, attention, transformers.

Lecture 9: Attention

Attention mechanisem, transformers, self supervised encoder-decoder, LLM and generative models like GPT

Additional Resources