Intro to DL Blended: различия между версиями

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'''Course program:'''
'''Course program:'''
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


'''Grading:'''
'''Grading:'''
Cumulative grade = 80% online course + 20% additional project
Cumulative grade = 80% online course + 20% additional project
Final grade = 75% cumulative grade + 25% final exam
Final grade = 75% cumulative grade + 25% final exam


'''Additional project:'''
'''Additional project:'''
Homework with Kaggle competition
Homework with Kaggle competition


'''Exam:'''
'''Exam:'''
In writing, theoretical questions, for instance:
In writing, theoretical questions, for instance:
# SGD variations: Moment, RMSProp, Adam with explanation
# SGD variations: Moment, RMSProp, Adam with explanation

Версия от 17:31, 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:

  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. Gradient of RNN cell (with proof)