Deep Learning Models (Applied Mathematics, 1.9)
Тип: На вибір студента
Кафедра: computational mathematics
Навчальний план
| Семестр | Кредити | Звітність |
| 9 | 6 | Залік |
Лекції
| Семестр | К-сть годин | Лектор | Група(и) |
| 9 | 32 | доцент Yu. A. Muzychuk | PMp-51m |
Лабораторні
| Семестр | К-сть годин | Група | Викладач(і) |
| 9 | 32 | PMp-51m | доцент Yu. A. Muzychuk |
Опис курсу
The course “Deep machine learning models” covers the following sections: Basics of machine learning, Convolutional neural networks for computer vision problems, and Modeling sequences with recurrent neural networks. When considering these topics, the main focus is on establishing the necessary mathematical apparatus for solving modern machine learning problems, researching neural network architectures for relevant practical problems, and using modern libraries and tools for programming of these algorithms. The presentation of the material is carried out using modern terms and concepts from the field of information technologies.
Рекомендована література
Primary Literature
1. Goodfellow I. Deep Learning / Ian Goodfellow, Yoshua Bengio, Aaron Courville. – MIT Press, 2016. – WWW: http://www.deeplearningbook.org
2. Howard J. Deep Learning for coders with Fastai and PyTorch: AI applications without a PhD / Jeremy Howard, Sylvain Gugger. – O’Reilly Media, 2020. – WWW: https://github.com/fastai/fastbook
3. Zhang A. Dive into Deep Learning / A.Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola. – arXiv preprint arXiv:2106.11342, 2021. ¬– WWW: https://arxiv.org/abs/2106.11342
4. Buduma N. Fundamentals of Deep Learning. Designing Next-Generation Machine Intelligence Algorithms. 2nd Edition / Nithin Buduma, Nikhil Buduma, Joe Papa. – O’Reilly, 2022. – 388 p.
5. Patterson J. Deep Learning: A Practitioner’s Approach / Josh Patterson, Adam Gibson. ¬– O’Reilly, 2017. – 352 p.
6. Burns S. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow / Samuel Burns. – 2019. – 176 p.
7. Papa J. PyTorch Pocket Reference: Building and Deploying Deep Learning Models / Joe Papa. – O’Reilly, 2021. – 307 p.
8. Falk K. Practical Recommender Systems / Kim Falk. – Manning, 2019. – 432 p.
Additional Literature
9. Ye A. Modern Deep Learning Design and Application Development / Andre Ye. – Apress, 2022. – 451p.
10. Foster D. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play / David Foster. – O’Reilly, 2019. – 330 p.
11. Hope T. Learning TensorFlow: A Guide to Building Deep Learning Systems / Tom Hope, Yehezkel Resheff, Itay Lieder. – O’Reilly, 2017. – 242 p.
12. Harrison P. Deep Learning with Text / Patrick Harrison, Matthew Honnibal. – O’Reilly, 2020. – 250 p.
13. Watson C. A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research / Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, Denys Poshyvanyk. – arXic preprint arXiv:2009.06520v2, 2021. – WWW: https://arxiv.org/pdf/2009.06520.pdf
14. Theobald O. Machine Learning: Make Your Own Recommender System / Oliver Theobald. – Scatterplot Press, 2018. – 129 p.