Глубинное обучение

2026/2027

Статус: Маго-лего

Кто читает: Департамент анализа данных и искусственного интеллекта

Где читается: Факультет компьютерных наук

Онлайн-часы: 48

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Ратников Федор Дмитриевич

Язык: русский

Кредиты: 6

Контактные часы: 48

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Аннотация

The proposed course is dedicated to the methods of "deep learning" — neural network methods of machine learning that have sparked a surge of development in several applied fields. Primarily, the course aims to develop students' skills in solving practical problems using deep neural networks. In recent years, deep learning methods have firmly established themselves in various applied areas, such as: - Computer Vision: recognizing visual patterns, segmentation, synthesizing images from text descriptions; - Text Processing: sentiment analysis, question-answer systems, machine translation, large language models; - Speech Processing: recognition, synthesis, classification of accents, tones, etc.; - Robotics: optimizing behavior strategies, Reinforcement Learning. In this course, students are briefly introduced to the applications of neural networks in all these

Цель освоения дисциплины

The proposed course is primarily aimed at developing practical skills in students and providing them with experience in solving applied problems using neural networks. This includes training, fine-tuning, analyzing, and effectively applying neural network models.

Планируемые результаты обучения

General skills in building and training neural network models.
Using neural networks for computer vision, working with convolutional and ViT-like neural networks
Using neural networks for text analysis, including mechanisms of attention.
Fine-tuning models trained on general data (transfer learning).
General knowledge in several other areas of neural network application