Deep Learning Course, 2025

This course provides a comprehensive exploration of modern deep learning techniques, from foundational concepts to advanced topics.

• Introduction to Neural Networks: MLP, Backpropagation, Initialization, Optimization, Regularization, CNN
• Natural Language Processing: Embeddings, RNN, LSTM, Attention, Transformer
• Computer Vision
• Reinforcement Learning
• Generative Models: Autoregression, VAE, GAN, Diffusion, Flow Matching
• Advanced NLP: LLM, RAG, Agents
• Acceleration: Compilation, Quantization, Distillation

Course Staff

• Eduard Vladimirov @Edyarich
• Daniil Dorin @DorinDaniil
• Nikita Kiselev @kisnikser
• Sergey Firsov @Schaft-s
• Vadim Kasiuk @KasiukVadim

Materials

Week #	Date	Topic	Lecture	Seminar	Recording
1	September, 9	MLP, Backpropagation	slides, slides with notes	ipynb	record
2	September, 16	Optimization, Regularization	slides	ipynb	record
3	September, 23	Initialization, Normalization, CNN	slides	ipynb, notes	lecture record, seminar record
4	September, 30	Intro to NLP, Embeddings	slides	ipynb	record
5	October, 7	RNN, LSTM, Attention, Transformer	slides	ipynb	record
6	October, 14	-	-	-	-
7	October, 21	-	-	-	-
8	October, 28	-	-	-	-
9	November, 4	-	-	-	-
10	November, 11	-	-	-	-
11	November, 18	-	-	-	-
12	November, 25	-	-	-	-
13	December, 2	-	-	-	-
14	December, 9	-	-	-	-

Homeworks

Homework #	Date	Deadline	Description	Link
1	September, 8	September, 29	Autograd implementation	google form
2	September, 8	October, 13	Alexnet implementation on PyTorch	google form
3	September, 8	October, 28	Image captioning with attention	google form
4	-	-	-	-
5	-	-	-	-
6	-	-	-	-

Game Rules

• 6 Homeworks = 70 points
• Oral Exam = 30 points
• Maximum Points: 70 + 30 = 100 points

Final Grade: min(round(#points/10), 10)

Prerequisities

• Probability Theory + Statistics
• Machine Learning
• Python

Previous Episodes

• 2024
• 2023
• 2022