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: Neural Networks
Linear and multilayer Perceptron, loss functions, activation functions, pooling, weight sharing, convolutional layers, gradient descent, backpropagation.
Lecture 3: Neural Networks II
advance architectures
Lecture 4: Optimization
Approximation, estimation and optimization errors, regularization, loss surface curvature, descentbased optimization methods, secondorder methods.
Lecture 5: Training regime
Training regime for Deep Learning
Lecture 6: Object Detection
CVbased approaches, RCNN, RPN, YOLO, SSD, losses, benchmarks and performance metrics.
Lecture 7: Sequence Models
RNN model, inputoutput sequences relationships, nonsequential input, layered RNN, backpropagation through time, word embeddings, attention, transformers.
Lecture 8: self supervision
Self supervision
Lecture 9: Attention
Attention mechanisem, transformers, self supervised encoderdecoder, LLM and generative models like GPT
Lecture 10: Vision transformers
Transformers in computer Vision, clip etc..
Lecture 11: Generative adverserial networks
Minimax and Nash equlibrium GAN, DCGAN, WGAN
Lecture 12: Variational AutoEncoders
variational inference, ELBO loss, reparametrization trick VAE, VQVAE and DallE
Lecture 13: Diffusion models
Diffusion process and models
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.