Lectures
The lecture presentations and the accompanying material for each lecture is posted here.
Lecture 1: Introduction
Origins of deep learning, course goals, overview of machinelearning paradigms.
Lecture 2: Multi Layer Perceptron
MLP as non linear deep model Intro to CNN
Lecture 3: Convolution Neural Nets
This lecture is about the revolutional Convolutional neural networks
Lecture 4+5: optimization
This lecture is about deep neural network optimization.
Lecture 6: Sequence Models
This lecture is about deep neural networks for sequence processing.
Lecture 7: Attention
This lecture is about the attention mechanism.
Lecture 8+9: Transformers
Transformers, LLM and ViT
Lecture 11: Diffusion Models
Video here
Additional Resources

From previus semesters, the videos of Alex lectures

The supplemental material page contains prerequisite topics you should be familiar with.

Detailed notes will be available for most lectures on the lecture notes page.