Bachelor
2021/2022
Introduction to Deep Learning
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Elective course (Applied Mathematics and Information Science)
Area of studies:
Applied Mathematics and Information Science
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
3 year, 1-3 module
Mode of studies:
distance learning
Online hours:
67
Open to:
students of one campus
Instructors:
Сухарева Анжелика Вячеславовна,
Чесаков Даниил Георгиевич,
Хайдуров Руслан Александрович,
Чесаков Даниил Георгиевич,
Сухарева Анжелика Вячеславовна,
Elena Kantonistova,
Alexey Kovalev,
Ildus Sadrtdinov,
Akim Tsvigun
Language:
English
ECTS credits:
6
Contact hours:
10
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 courseThere is no exam. The course grade is given according to the cumulative assessment.
- TestsThere is no exam. The course grade is given according to the cumulative assessment.
- HomeworksThere is no exam. The course grade is given according to the cumulative assessment.
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