Introduction to Deep Learning

2021/2022

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Big Data and Information Retrieval School

Course type:

Elective course

When:

3 year, 1-3 module

Instructors

Kantonistova, Elena

Kovalev, Alexey

Sadrtdinov, Ildus

Сухарева Анжелика Вячеславовна

Хайдуров Руслан Александрович

Tsvigun, Akim

Чесаков Даниил Георгиевич

Course Syllabus

Abstract

The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The course is based on MOOC “Introduction to deep learning”: https://ru.coursera.org/learn/intro-to-deep-learning.

Learning Objectives

• To familiarize students with the basic concepts, models and algorithms of neural networks

Expected Learning Outcomes

• Have skills in training and applying basic neural network models
• Know principles of neural network models

Course Contents

• Introduction to optimization
• Introduction to neural networks
• Deep Learning for images
• Unsupervised representation learning
• Deep learning for sequences
• First programming project
• Second programming project

Assessment Elements

• Online course
  There is no exam. The course grade is given according to the cumulative assessment.
• Tests
  There is no exam. The course grade is given according to the cumulative assessment.
• Homeworks
  There is no exam. The course grade is given according to the cumulative assessment.

Interim Assessment

• 2021/2022 3rd module
  0.4 * Homeworks + 0.3 * Tests + 0.3 * Online course

Bibliography

Recommended Core Bibliography

• Гудфеллоу, Я. Глубокое обучение / Я. Гудфеллоу, И. Бенджио, А. Курвилль ; перевод с английского А. А. Слинкина. — 2-е изд. — Москва : ДМК Пресс, 2018. — 652 с. — ISBN 978-5-97060-618-6. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/107901 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.

Recommended Additional Bibliography

• Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705