Intro to DL Blended — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Zimovnov (обсуждение | вклад) |
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# Description of auto-encoder, application to images | # Description of auto-encoder, application to images | ||
# Gradient of RNN cell (with proof) | # Gradient of RNN cell (with proof) | ||
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| + | https://colab.research.google.com/drive/1HoEsK580KAzMGuvFyYwUFdnRzuZ_hC13 | ||
Версия 17:03, 5 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
Release date: 10-03-2019
Deadline: 24-03-2019
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
- Description of auto-encoder, application to images
- Gradient of RNN cell (with proof)
https://colab.research.google.com/drive/1HoEsK580KAzMGuvFyYwUFdnRzuZ_hC13
