Intro to DL Blended — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Zimovnov (обсуждение | вклад) (Новая страница: «'''Program:''' https://www.hse.ru/data/2018/06/05/1150113338/program-2129241367-JndYcQjSAq.pdf '''Grading:''' Cumulative grade = 80% online course + 20% addition…») |
Zimovnov (обсуждение | вклад) |
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https://www.hse.ru/data/2018/06/05/1150113338/program-2129241367-JndYcQjSAq.pdf | https://www.hse.ru/data/2018/06/05/1150113338/program-2129241367-JndYcQjSAq.pdf | ||
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'''Additional project:''' | '''Additional project:''' | ||
Homework with Kaggle competition | 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) | ||
Версия 20:30, 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)
