Syllabus

Please find all the Video Lectures at this YouTube Playlist.

Lecture Slides are password protected.

Week 01: Introduction to EE546, Sequence Models, Variable Length Data, Sequence Problems,(Slides)
Week 02: Introduction to Recurrent Neural Networks, Vanilla RNN, Types of RNN, (Slides) (VanillaRNN.py) (Alternative Slides - Toronto Uni.)
Week 03: Training RNNs, Backpropagation Through Time, Vanishing Gradient Problem, (Slides) (Alternative Slides - Toronto Uni.)
Week 04: Gated Units, LSTMs, GRUs, (Slides)
pyrtorch Tutorial on RNN+LSTM+GRUs, codes of the tutorial
Week 05: Bidirectional Recurrent Networks, Bidirectional Gated units, BiLSTMs and Types of LSTMs, (Slides)
Week 06: Deep RNNs and CNN+RNN Architectures, (Slides)
Week 07: Midterm Week
Week 08: 23rd April - National Holiday
Week 09: Introduction to Natural Language Processing, Word Embeddings and Language Sequences (Slides)
Week10: Attention Concept and Transformers (Slides)
Week 11: Bayram Week, no Lectures.
Week 12: Visual attention, Vision Transformers and some examples of RNN-based trackers (Slides)
Week 13: Project Presentations (part I)
Week 14: Project Presentations (part II)

EE546