Neural Networks 2
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
818NES2 | Z | 2 | 1+1 | Czech |
- Garant předmětu:
- Lecturer:
- Tutor:
- Supervisor:
- Department of Software Engineering
- Synopsis:
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The second module is oriented first to multi-layer neural networks and next to self-organized artificial neural networks. The biological context, cluster analysis and principal component analysis are used for self-organized artificial neural network realization. Self-organization is discussed both in vector and metric spaces.
- Requirements:
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Basic knowledges from linear algebra.
- Syllabus of lectures:
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1.Multi-layer perceptron, universal approximation, backpropagation.
2.Vector space with Minkowski metrics.
3.Metric space of strings.
4.Cluster analysis in vector space.
5.Cluster analysis in metric space.
6.Self-organization, patterns, etalons.
7.SOM as extended cluster analysis.
8.SOM topology, SOM as transformation.
9.Kohonen learning of SOM.
10.SOM learning in metric space.
11.Traditional principal component analysis.
12. Introduction to deep learning.
- Syllabus of tutorials:
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1.Multi-layer perceptron, universal approximation, backpropagation.
2.Vector space with Minkowski metrics.
3.Metric space of strings.
4.Cluster analysis in vector space.
5.Cluster analysis in metric space.
6.Self-organization, patterns, etalons.
7.SOM as extended cluster analysis.
8.SOM topology, SOM as transformation.
9.Kohonen learning of SOM.
10.SOM learning in metric space.
11.Traditional principal component analysis.
12. Introduction to deep learning.
- Study Objective:
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Knowledge:
Mutli-layer perceptron, cluster analysis, self-organized artificial neural networks, basis of deep learning.
Abilities:
Use of mutli-layer perceptron, use of cluster analysis, use of self-organization, use of deep learning for classification.
- Study materials:
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Compulsory literature:
[1] J. Šíma, R. Neruda: Teoretické otázky neuronových sítí, Matfyzpress, Praha, 1996.
[2] M. Šnorek: Neuronové sítě a neuropočítače, ČVUT, Praha 2002
Recommended literature:
[3] S. Haykin: Neural Networks, Macmillan, New York, 1994.
[4] L.V. Fausett: Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, New Jersey, 1994.
- Note:
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans:
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- Applications of Informatics in Natural Sciences (elective course)