Магистратура
2026/2027
Введение в глубокое обучение для АОТ
Статус:
Курс обязательный (Компьютерная лингвистика)
Кто читает:
Школа лингвистики
Где читается:
Факультет гуманитарных наук
Когда читается:
2-й курс, 1, 2 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
6
Контактные часы:
56
Course Syllabus
Abstract
Introduction to Deep Learning https://www.coursera.org/learn/intro-to-deep-learning?specialization=aml 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 prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models.
Learning Objectives
- 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.
Expected Learning Outcomes
- Module 2: Introduction to neural networks
- Module 3: Deep Learning for images
- Module 4: Unsupervised representation learning
- Module 5: Deep learning for sequences
Course Contents
- Module 1: Introduction to optimization
- Module 2: Introduction to neural networks
- Module 3: Deep Learning for images
- Module 4: Unsupervised representation learning
- Module 5: Deep learning for sequences
