Intro to DL Blended

Course program:

https://www.hse.ru/data/2018/06/05/1150113338/program-2129241367-JndYcQjSAq.pdf

Grading:

Cumulative grade = 80% online course + 20% additional project

Final grade = 75% cumulative grade + 25% final exam

Additional project:

Homework with Kaggle competition

Release date: 10-03-2019

Deadline: 24-03-2019

Exam:

In writing, theoretical questions, for instance:

1. SGD variations: Moment, RMSProp, Adam with explanation
2. Description of backprop and proof of its efficiency
3. Gradient of a dense layer in matrix notation (with proof)
4. Typical CNN architecture, purpose of each layer, how to do backprop
5. Inception V3 architecture choices
6. Description of auto-encoder, application to images
7. Gradient of RNN cell (with proof)