Просмотр исходного текста страницы 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: https://docs.google.com/document/d/1kTMYq21UFqZOqftjKAPq8G7RRkO7kX3MomsVIVhW830/edit?usp=sharing

Release date: 10-03-2019 16:00

Deadline: 24-03-2019 03:00

'''Exam:'''

In writing, theoretical questions, for instance:
# SGD variations: Moment, RMSProp, Adam with explanation
# Description of backprop and proof of its efficiency (линейное время работы)
# 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)

'''Семинары:'''

1. Keras Tutorial https://colab.research.google.com/drive/1HoEsK580KAzMGuvFyYwUFdnRzuZ_hC13