• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта

Глубокое обучение

2020/2021
Учебный год
RUS
Обучение ведется на русском языке
6
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 1, 2 модуль

Преподаватель

Программа дисциплины

Аннотация

Discipline of choice. Deep learning is a popular area that uses neural networks of complex architecture. Such systems give better results in areas such as image and video processing, sound and text. The course will cover the main types of architectures and the principles of operation and training of deep neural networks and conduct practice in the above areas of application. As a result of mastering the discipline, the student must:  Know the methods of building deep neural networks;  Be able to apply deep learning to solve specific tasks;  own a mathematical apparatus and algorithms for working with deep neural networks.
Цель освоения дисциплины

Цель освоения дисциплины

  • The objectives of mastering the discipline "Deep Learning" are the formation of students' theoretical knowledge and practical skills on the basics of building large neural networks for deep learning.
Планируемые результаты обучения

Планируемые результаты обучения

  • Knows optimization algorithms for deep neural networks based on various variations of gradient descent. Configures such algorithms based on the conditions of a specific task
  • Knows the basic architecture of neural networks used to classify images. Modifies layers and various parameters to solve applied problems. It uses networks to solve the problems of image classification, image segmentation and video stream.
  • He knows the general, general scientific and business vocabulary used in the field of deep learning. It receives from articles (including in English) information about the structure of the neural network and the features used to solve a specific problem. Describes, presents and analyzes the results of applying deep learning methods to solve applied problems.
  • He knows the main types of tasks solved using deep learning. Develops architecture, implements, trains and optimizes the parameters of neural networks. It solves applied problems from various fields using deep learning.
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Optimization and Regularization Algorithms
  • Image Processing and Analysis
  • Natural language processing, competitive and generative neural networks
  • Hyperparameter optimization, reinforcement learning
Элементы контроля

Элементы контроля

  • неблокирующий Homework 1
  • неблокирующий Homework 2
  • неблокирующий Homework 3
  • блокирующий Exam
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (2 модуль)
    0.5 * Exam + 0.16 * Homework 1 + 0.17 * Homework 2 + 0.17 * Homework 3
Список литературы

Список литературы

Рекомендуемая основная литература

  • Iba, H. (2018). Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-Evolution and Gene Regulatory Networks. Singapore: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1833749

Рекомендуемая дополнительная литература

  • Taweh Beysolow II. (2017). Introduction to Deep Learning Using R. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.1.4842.2734.3