Practical Deep Learning

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Code Completion Credits Range Language
NIE-PDL KZ 5 2P+1C English
Garant předmětu:
Karel Klouda
Yauhen Babakhin, Martin Barus
Yauhen Babakhin, Martin Barus
Department of Applied Mathematics

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.


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

Syllabus of lectures:

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

Syllabus of tutorials:

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

Study Objective:

The study objective of the course is to equip students with practical skills and knowledge in building and training deep neural networks using PyTorch.

Study materials:

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).

Further information:
Time-table for winter semester 2024/2025:
Time-table is not available yet
Time-table for summer semester 2024/2025:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-04-19
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