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Обычная версия сайта
Книга
Integral Robot Technologies and Speech Behavior

Kharlamov A. A., Pantiukhin D., Borisov V. et al.

Newcastle upon Tyne: Cambridge Scholars Publishing, 2024.

Статья
Clique detection with a given reliability

Semenov D., Koldanov A. P., Koldanov P. et al.

Annals of Mathematics and Artificial Intelligence. 2024.

Глава в книге
Neural Networks for Speech Synthesis of Voice Assistants and Singing Machines

Pantiukhin D.

In bk.: Integral Robot Technologies and Speech Behavior. Newcastle upon Tyne: Cambridge Scholars Publishing, 2024. Ch. 9. P. 281-296.

Препринт
DAREL: Data Reduction with Losses for Training Acceleration of Real and Hypercomplex Neural Networks

Demidovskij A., Трутнев А. И., Тугарев А. М. et al.

NeurIPS 2023 Workshop. ZmuLcqwzkl. OpenReview, 2023

Project seminar "Deep learning for computer vision"

2023/2024
Учебный год
ENG
Обучение ведется на английском языке
9
Кредиты

Преподаватель

Рассадин Александр Георгиевич

Рассадин Александр Георгиевич

Course Syllabus

Abstract

This course covers basics of Deep Learning approaches applied to the popular Computer Vision tasks. The aim of the course is to highlight how the modern 2D Computer Vision works and how the area evolved to this. Students will learn the main concepts of the modern 2D Computer Vision, how to master some popular applied tasks and try by themselves. Every week, the students will learn a standalone chapter of Computer Vision starting from the Image Classification approaches, through Object Detection, Image Segmentation and ending with some cutting-edge and real-life approaches. The students also will introduced to the popular datasets for each task along with the common quality estimation procedures and open source solutions.
Learning Objectives

Learning Objectives

  • Students will learn how to use pretrained image classifiers and build their own
  • Students will touch with modern Deep Learning frameworks
  • Students will build their first neural networks models
Expected Learning Outcomes

Expected Learning Outcomes

  • Students will touch with modern Deep Learning frameworks
  • Students will build their first neural networks model
  • Students will learn how to use pretrained image classifiers and build their own
  • Students will learn how to use pretrained object detector and build their own
  • Students will learn how to use existing REID models and build their own
  • Students will learn how to track their objects
  • Students will learn how to use pretrained image segmentation model and build their own
  • Students will attempt to create a prototype of real-life video surveillance solution
Course Contents

Course Contents

  • Introduction to Deep Learning.
  • Deep Learning for Image Classification
  • Deep Object Detection
  • Deep Object Tracking and Person Reidentification
  • Deep Image Segmentation
  • Final project
Assessment Elements

Assessment Elements

  • non-blocking test
  • non-blocking test2
  • non-blocking EXAM
Interim Assessment

Interim Assessment

  • 2023/2024 4th module
    0.5 * EXAM + 0 * test2 + 0.5 * test
Bibliography

Bibliography

Recommended Core Bibliography

  • Introduction to deep learning, Charniak, E., 2018

Recommended Additional Bibliography

  • Deep learning, Kelleher, J. D., 2019
  • Image analysis, classification, and change detection in remote sensing : with algorithms for Python, Canty, M. J., 2019