Введение в глубинное обучение
- To familiarize students with the basic concepts, models and algorithms of neural networks
- Know principles of neural network models
- Have skills in training and applying basic neural network models
- Introduction to optimizationIn the first week you'll learn about linear models and stochatic optimization methods. Linear models are basic building blocks for many deep architectures, and stochastic optimization is used to learn every model that we'll discuss in our course.
- Introduction to neural networksThis module is an introduction to the concept of a deep neural network. You'll begin with the linear model and finish with writing your very first deep network.
- Deep Learning for imagesIn this week you will learn about building blocks of deep learning for image input. You will learn how to build Convolutional Neural Network (CNN) architectures with these blocks and how to quickly solve a new task using so-called pre-trained models.
- Unsupervised representation learningThis week we're gonna dive into unsupervised parts of deep learning. You'll learn how to generate, morph and search images with deep learning.
- Deep learning for sequencesIn this week you will learn how to use deep learning for sequences such as texts, video, audio, etc. You will learn about several Recurrent Neural Network (RNN) architectures and how to apply them for different tasks with sequential input/output.
- First programming projectIn this week you will apply all your knowledge about neural networks for images and texts for the project. You will solve the task of generating descriptions for real world images!
- Second programming projectFinal project
- Online courseThere is no exam. The course grade is given according to the cumulative assessment.
- TestsThere is no exam. The course grade is given according to the cumulative assessment.
- HomeworksThere is no exam. The course grade is given according to the cumulative assessment.
- Гудфеллоу Я., Бенджио И., Курвилль А. - Глубокое обучение - Издательство "ДМК Пресс" - 2018 - 652с. - ISBN: 978-5-97060-618-6 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/107901
- Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705