Master
2024/2025
Introduction to Deep Learning in NLP
Type:
Elective course (Applied Artificial Intelligence Models)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
School of Linguistics
Mode of studies:
offline
Open to:
students of all HSE University campuses
Master’s programme:
Applied Artificial Intelligence Models
Language:
English
ECTS credits:
6
Contact hours:
32
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.