AGH University of Science and Technology
Krakow, Poland
Adrian Horzyk
horzyk@agh.edu.pl
4
5 3
2
1
5 3
2
1
4
5 3
2
1
5 3
2
1
4
5 3
2
1
4
5 3
2 1
4
5 3
2 1
4
5 3
2 1
4
5 3
2 1
4
5 3
2
1
4
5 3
2 1
4
5 3
2 1
4
5 3
2 1
4
5 3
2 1
4
5 3
2
1
4
5 3
2 1
4
5 3
2 1
4
5 3
2 1
4
5 3
2 1
5 3
2
1
swi
5.8 2.6
sle
5.0 5.4 6.3 2.3 3.0 3.3
R2
R1 R3 R4
6.7
6.0 2.2 2.7
4.9 2.5
R6
R5 R8 R9
5.9 3.2
R7
pwi
4.0 1.2
ple
3.3 4.5 4.7 4.8 5.0 5.1 1.0 1.5 1.6 1.7 1.8 class
VERSI
5.7 6.5
R10 R11 R12 R13
2.0 VIRGIN 1.9
param
AGDS
swi 5.8 2.6 sle 5.0 2.3
5.4 3.0 6.3 3.3 R2
R1 R3 R4
IRI S P AT T ERN S
6.7 3.0 6.0 2.7
6.0 2.2 4.9 2.5 R6
R5
R8 R9
5.9 3.2 R7
pwi 4.0 1.2 ple 3.3 1.0
4.5 1.5 4.7 1.6 5.0 1.7 5.1 1.6
5.0 1.5 4.5 1.7 4.8 1.8
class VERSI VERSI
VERSI VERSI VERSI VERSI
VIRGIN VIRGIN VERSI
6.0 3.0
5.7 2.5 6.5 3.2 R10
R12 R13
5.8 2.7 R11
4.8 1.8
5.0 2.0 5.1 2.0 5.1 1.9
VIRGIN
VIRGIN VIRGIN VIRGIN param
swi
sle ple pwi class
param
swi sle
R2
R1 R3 R4 R5 R6 R7 R8 R9
pwi
ple class
R10 R11 R12 R13 param
swi
5.8 2.6
sle
5.0 5.4 6.3 2.3 3.0 3.3
R2
R1 R3 R4
IRIS PATTERNS
in the tree-based graph structure
6.7
6.0 2.2 2.7
4.9 2.5
R6
R5 R8 R9
5.9 3.2
R7
pwi
4.0 1.2
ple
3.3 4.5 4.7 4.8 5.0 5.1 1.0 1.5 1.6 1.7 1.8 class
VERSI
5.7 6.5
R10 R11 R12 R13
2.0 VIRGIN 1.9
param
AGDS
ASSOCIATIVE TRANSFORMATION
5.8 2.6 5.0 2.3
5.4 3.0 6.3 3.3 R2
R1 R3 R4
IRI S P AT T ERN S
6.7 3.0 6.0 2.7
6.0 2.2 4.9 2.5 R6
R5
R8 R9
5.9 3.2 R7
4.0 1.2 3.3 1.0
4.5 1.5 4.7 1.6 5.0 1.7 5.1 1.6
5.0 1.5 4.5 1.7 4.8 1.8
VERSI VERSI
VERSI VERSI VERSI VERSI
VIRGIN VIRGIN VERSI
6.0 3.0
5.7 2.5 6.5 3.2 R10
R12 R13
5.8 2.7
R11 4.8 1.8
5.0 2.0 5.1 2.0 5.1 1.9
VIRGIN
VIRGIN VIRGIN VIRGIN
5.8 2.6 5.0 2.3
5.4 3.0 6.3 3.3 6.7 3.0 6.0 2.7
6.0 2.2 4.9 2.5 5.9 3.2
4.0 1.2 3.3 1.0
4.5 1.5 4.7 1.6 5.0 1.7 5.1 1.6
5.0 1.5 4.5 1.7 4.8 1.8
VERSI VERSI
VERSI VERSI VERSI VERSI
VIRGIN VIRGIN VERSI
6.0 3.0
5.7 2.5 6.5 3.2 5.8 2.7
4.8 1.8
5.0 2.0 5.1 2.0 5.1 1.9
VIRGIN
VIRGIN VIRGIN VIRGIN
swi
sle ple pwi class
param
5.8 2.6 5.0 2.3 5.4
6.3 3.3 6.7
3.0 6.0
2.7 2.2 4.9
5.9 3.2
4.0 1.2 3.3 1.0
4.5 1.5 4.7 1.6 5.0
1.7 5.1 4.8
1.8
VERSI
5.7 2.5
6.5
2.0 1.9
VIRGIN
swi
5.8 2.6
sle
5.05.4 6.3 2.3 3.0 3.3
R2
R1 R3 R4
6.7
6.0 2.2 2.7
4.9 2.5
R6
R5 R8 R9
5.9 3.2
R7
pwi
4.0 1.2
ple
3.3 4.54.7 4.85.0 5.11.0 1.5 1.6 1.71.8 class
VERSI
5.7 6.5
R10 R11 R12 R13
2.0 VIRGIN 1.9
param
AGDS
Let’s start with powerful computations!
Bibliography and Literature
1. Nikola K. Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, In Springer Series on Bio- and Neurosystems, Vol 7., Springer, 2019.
2. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016, ISBN 978-1-59327-741-3 or PWN 2018.
3. Holk Cruse, Neural Networks as Cybernetic Systems, 2nd and revised edition 4. R. Rojas, Neural Networks, Springer-Verlag, Berlin, 1996.
5. Convolutional Neural Network (Stanford)
6. Visualizing and Understanding Convolutional Networks, Zeiler, Fergus, ECCV 2014
7. IBM: https://www.ibm.com/developerworks/library/ba-data-becomes-knowledge-1/index.html 8. NVIDIA: https://developer.nvidia.com/discover/convolutional-neural-network
9. JUPYTER: https://jupyter.org/
10. https://www.youtube.com/watch?v=XNKeayZW4dY 11. https://victorzhou.com/blog/keras-cnn-tutorial/
12. https://github.com/keras-team/keras/tree/master/examples
13. https://medium.com/@margaretmz/anaconda-jupyter-notebook-tensorflow-and-keras-b91f381405f8 14. https://blog.tensorflow.org/2019/09/tensorflow-20-is-now-available.html
15. http://coursera.org/specializations/tensorflow-in-practice
16. https://udacity.com/course/intro-to-tensorflow-for-deep-learning
17. MNIST sample: https://medium.com/datadriveninvestor/image-processing-for-mnist-using-keras- f9a1021f6ef0
18. Heatmaps: https://towardsdatascience.com/formatting-tips-for-correlation-heatmaps-in-seaborn- 4478ef15d87f
