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 machine-learning 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 7: Transformers
This one is about the Transformer architecture: “attention is all you need” and more.
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.