| Dima Bykhovsky

Deep learning course, 2024-5

Note: the schedule is subject to change during the semester.

Week	Date	Mode	Theory (Coursera)	Practice
1	07/11	In-class meeting
2	14/11	Self-study	C1W1,C1W2	Fundamentals of Accelerated Computing with CUDA Python
3	21/11	Self-study	C1W3,C1W4
4	28/11	Self-study	C2W1,C1W2
5	05/12	Self-study	C2W3	Introduction to PyTorch
6	12/12	Self-study	C3W1, C3W2
7	19/12	Self-study	C4W1
8	26/12	Self-study	C4W2
9	02/01	Self-study		Open Source Models with Hugging Face
10	09/01	Self-study		Getting Started with Deep Learning
11	16/01	Presentation
12	23/01	-
13	30/01	Midterm

Coursera - Deep learning specialization: Use free videos or free “audit” registration per course (certification is voluntary and does not influence the grade). The following links are for the corresponding YouTube playlists:
- Neural Networks and Deep Learning (C1) YouTube
- Improving Deep Neural Networks (C2) (YouTube)
- Structuring Machine Learning Projects (C3) (YouTube)
- Convolutional Neural Networks (C4) (YouTube)
- Example of all the exercise solutions (link)
Microsoft Learn: Introduction to PyTorch
Microsoft Learn: Introduction to Computer Vision with PyTorch
Deep Learning Institute (Nvidia): Fundamentals of Accelerated Computing with CUDA Python - voucher code will be provided
Deep Learning Institute (Nvidia): Getting Started with Deep Learning - voucher code will be provided
DeepLearning.AI: Open Source Models with Hugging Face

Midterm exam - 30%
Online courses:
- Fundamentals of Accelerated Computing with CUDA Python - 15%
- Getting Started with Deep Learning - 15%
Presentation and summary - 20%
Participation and/or timely declaration of self-learning material - 20%

The details on the homework submission and participation/declaration are provided on the Moodle page.

Previous exams: