Course description

Main page | Study Branches/Specializations | Groups of Courses | All Courses | Roles Instructions

A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

NIE-PDL Practical Deep Learning Extent of teaching: 2P+1C

Instructor: Babakhin Y., Barus M. Completion: KZ

Department: 18105 Credits: 5 Semester: Z

Annotation:
This course is designed to provide students with a comprehensive understanding of Deep Learning using PyTorch, a popular open-source machine learning framework. Throughout the course, students will develop practical skills in building and training deep neural networks, using PyTorch to solve real-world problems in fields such as computer vision and natural language processing.

Lecture syllabus:

1. Introduction to PyTorch

2. Datasets and Dataloaders for Natural Language Processing (NLP)

3. NLP Architectures

4. PyTorch training loop

5. Train an NLP classification model

6. Evaluate NLP Kaggle competition

7. Datasets and Dataloaders for Computer Vision (CV)

8. CV Architectures

9. Train a CV classification model

10. Practical tips for tuning Deep Learning models

11. Multi-GPU training in PyTorch

12. Other applications in NLP and CV

13. Evaluate CV Kaggle competition

Seminar syllabus:

1. Introduction to PyTorch, Datasets and Dataloaders for NLP

2. PyTorch training loop

3. Train an NLP classification model

4. Datasets and Dataloaders for Computer Vision (CV), CV Architecture

5. Train a CV classification model

6. Multi-GPU training in PyTorch

Literature:

1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

2. Stevens, E., Antiga, L., & Viehmann, T. (2020). Deep Learning with PyTorch. Manning Publications.

3. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017).

4. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012).

Requirements:
Basic knowledge of Python programming language, basic understanding of machine learning and deep learning concepts.

Information about course and coursware are available at https://courses.fit.cvut.cz/NIE-PDL/

The course is also part of the following Study plans:

Study Plan Study Branch/Specialization Role Recommended semester

NI-TI.2018 Computer Science V Není

NI-PB.2020 Computer Security V Není

NI-ZI.2020 Knowledge Engineering V Není

NI-SPOL.2020 Unspecified Branch/Specialisation of Study V Není

NI-TI.2020 Computer Science V Není

NI-TI.2023 Computer Science V Není

NI-NPVS.2020 Design and Programming of Embedded Systems V Není

NI-PSS.2020 Computer Systems and Networks V Není

NI-MI.2020 Managerial Informatics V Není

NI-SI.2020 Software Engineering (in Czech) V Není

NI-SP.2020 System Programming V Není

NI-WI.2020 Web Engineering V Není

NI-SP.2023 System Programming V Není

NIE-SI.21 Software Engineering 2021 V Není

NIE-TI.21 Computer Science 2021 V Není

NIE-DBE.2023 Digital Business Engineering V Není

NIE-NPVS.21 Design and Programming of Embedded Systems 2021 V Není

NIE-PSS.21 Computer Systems and Networks 2021 V Není

NIE-PB.21 Computer Security 2021 V Není

Page updated 18. 4. 2024, semester: L/2020-1, L/2023-4, L/2019-20, Z/2021-2, L/2022-3, Z/2023-4, Z/2019-20, Z/2022-3, L/2021-2, Z/2024-5, Z/2020-1, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška

NIE-PDL	Practical Deep Learning			Extent of teaching:	2P+1C
Instructor:	Babakhin Y., Barus M.			Completion:	KZ
Department:	18105	Credits:	5	Semester:	Z

1.		Introduction to PyTorch
2.		Datasets and Dataloaders for Natural Language Processing (NLP)
3.		NLP Architectures
4.		PyTorch training loop
5.		Train an NLP classification model
6.		Evaluate NLP Kaggle competition
7.		Datasets and Dataloaders for Computer Vision (CV)
8.		CV Architectures
9.		Train a CV classification model
10.		Practical tips for tuning Deep Learning models
11.		Multi-GPU training in PyTorch
12.		Other applications in NLP and CV
13.		Evaluate CV Kaggle competition

1.		Introduction to PyTorch, Datasets and Dataloaders for NLP
2.		PyTorch training loop
3.		Train an NLP classification model
4.		Datasets and Dataloaders for Computer Vision (CV), CV Architecture
5.		Train a CV classification model
6.		Multi-GPU training in PyTorch

1.		Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
2.		Stevens, E., Antiga, L., & Viehmann, T. (2020). Deep Learning with PyTorch. Manning Publications.
3.		Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017).
4.		Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012).