Intro to DL Blended — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Zimovnov (обсуждение | вклад) |
Zimovnov (обсуждение | вклад) |
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| Строка 11: | Строка 11: | ||
'''Exam:''' | '''Exam:''' | ||
In writing, theoretical questions, for instance: | In writing, theoretical questions, for instance: | ||
| − | + | # SGD variations: Moment, RMSProp, Adam with explanation | |
| − | + | # Description of backprop and proof of its efficiency | |
| − | + | # Gradient of a dense layer in matrix notation (with proof) | |
| − | + | # Typical CNN architecture, purpose of each layer, how to do backprop | |
| − | + | # Inception V3 architecture choices | |
| − | + | # Gradient of RNN cell (with proof) | |
Версия 20:31, 3 марта 2019
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
Exam: In writing, theoretical questions, for instance:
- SGD variations: Moment, RMSProp, Adam with explanation
- Description of backprop and proof of its efficiency
- Gradient of a dense layer in matrix notation (with proof)
- Typical CNN architecture, purpose of each layer, how to do backprop
- Inception V3 architecture choices
- Gradient of RNN cell (with proof)
