Deep Learning Tools (SE)
Type: For the student's choice
Department: system design
Curriculum
| Semester | Credits | Reporting |
| 7 | 5 | Setoff |
Lectures
| Semester | Amount of hours | Lecturer | Group(s) |
| 7 | 32 | Parubochyi V. O. | ФеП-41 |
Laboratory works
| Semester | Amount of hours | Group | Teacher(s) |
| 7 | 32 | ФеП-41 | Parubochyi V. O. |
Опис навчальної дисципліни
The course “Deep Learning Tools” examines the basic concepts, goals and problems of artificial neural networks and deep learning, the principles of building different types of deep artificial networks and their application to solving applied machine learning problems. The main attention is paid to the assimilation of knowledge and the acquisition of skills corresponding to the current state of development of machine and deep learning, the ability to apply the acquired knowledge practically.
The academic discipline’s subject of study is artificial neural networks and deep learning, their concepts, problems, methods and tools for solving machine learning problems.
A laboratory course is provided to consolidate theoretical knowledge.
More detailed information about the course can be found on the course page in Moodle.
Recommended Literature
- A. C. Muller and S. Guido, Introduction to Machine Learning with Python, First Edition. Sebastopol, CA: O’Reilly Media, Inc., 2016. ISBN: 978-1-449-36941-5.
- F. Chollet, Deep Learning with Python, Second edition. Shelter Island, NY, USA: Manning Publications Co., 2021. ISBN: 978-1-61729-686-4.
- A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Third edition. Sebastopol, CA: O’Reilly Media, Inc., 2023. ISBN: 978-1-098-12597-4.
- I. Goodfellow, Y. Bengio, and A. Couville, Deep Learning. Cambridge, MA, USA: The MIT Press, 2016. ISBN: 978-0-262-03561-3. Available: https://www.deeplearningbook.org
- K. P. Murphy, Probabilistic Machine Learning: An Introduction. Cambridge, MA, USA: The MIT Press, 2022. ISBN: 978-0-262-36930-5. Available: https://probml.github.io/pml-book/book1.html
- J. VanderPlas, Python Data Science Handbook, Second Edition. Sebastopol, CA: O’Reilly Media, Inc., 2023. ISBN: 978-1-098-12122-8.
- P. Baheti, “A Newbie-Friendly Guide to Transfer Learning,” V7Labs Blog, Oct. 12, 2021. [Online]. Available: https://www.v7labs.com/blog/transfer-learning-guide
- A. Amidi and S. Amidi, “Recurrent Neural Networks cheatsheet,” CS 230 – Deep Learning cheatsheets, Stanford University. [Online]. Available: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
- C. Olah, “Understanding LSTM Networks,” colah’s blog, Aug. 27, 2015. [Online]. Available: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
- R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd Edition. Melbourne, AU: OTexts, 2021. ISBN: 978-0-987-50713-6. Available: https://otexts.com/fpp3/
- K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: The MIT Press, 2012. ISBN: 978-0-262-01802-9. Available: https://probml.github.io/pml-book/book0.html
- K. P. Murphy, Probabilistic Machine Learning: Advanced Topics. Cambridge, MA, USA: The MIT Press, 2023. ISBN: 978-0-262-04843-9. Available: https://probml.github.io/pml-book/book2.html
- A. Amidi and S. Amidi, “CS 230 – Deep Learning cheatsheets,” Stanford University. [Online]. Available: https://stanford.edu/~shervine/teaching/cs-230/