- The objectives of mastering the discipline "Deep Learning" are the formation of students' theoretical knowledge and practical skills on the basics of building large neural networks for deep learning.
- Knows optimization algorithms for deep neural networks based on various variations of gradient descent. Configures such algorithms based on the conditions of a specific task
- Knows the basic architecture of neural networks used to classify images. Modifies layers and various parameters to solve applied problems. It uses networks to solve the problems of image classification, image segmentation and video stream.
- He knows the general, general scientific and business vocabulary used in the field of deep learning. It receives from articles (including in English) information about the structure of the neural network and the features used to solve a specific problem. Describes, presents and analyzes the results of applying deep learning methods to solve applied problems.
- He knows the main types of tasks solved using deep learning. Develops architecture, implements, trains and optimizes the parameters of neural networks. It solves applied problems from various fields using deep learning.
- Optimization and Regularization Algorithms
- Image Processing and Analysis
- Natural language processing, competitive and generative neural networks
- Hyperparameter optimization, reinforcement learning
- Interim assessment (2 module)0.5 * Exam + 0.16 * Homework 1 + 0.17 * Homework 2 + 0.17 * Homework 3
- Iba, H. (2018). Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-Evolution and Gene Regulatory Networks. Singapore: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1833749
- Taweh Beysolow II. (2017). Introduction to Deep Learning Using R. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.1.4842.2734.3