This document discusses machine learning and artificial intelligence techniques, including pattern recognition in data, intuitive estimation, and examples of applications like credit risk assessment. It describes demos of image and function recognition models built with deep neural networks and k-means clustering. The document promotes a company that provides digital transformation and Microsoft Azure services, including AI/ML model training and integration in cloud environments.

1. daenet Gesellschaft für Informationstechnologie mbH
Hanauer Landstr. 204 60314 Frankfurt am Main
KI zum Anfassen
Damir Dobric
daenet GmbH
https://daenet.de

2. https://about.me/damirdobric

3. • Intro
• Patterns in Daten
• Intuitive Schätzung
• Beispiele:
• Wenn die Intuition nicht mehr hilft
• Arbeiten mit Produktionsdaten
• Kreditrisiko
• Demos
• ML/KI Unter der Haube
• Image Recognition mit DNN
• Function Recognition mit K-Means
• Q&A

4. Ihr Partner für digitale Transformation
und Microsoft Azure!
Cloud IoTAI / ML

5. Gegründet 1999
Sitz Frankfurt am Main
30 Mitarbeiter
Unternehmensstandort
und Nearshore
Microsoft
Partner of the
Year Award
2005 & 2010
Starke
Microsoft
Partnerschaft
Gold Cloud Platform
Gold Application Development
Gold Application Integration
Azure Advisors Program
Microsoft Most Valuable Professional
Microsoft Regional Director
Initiative
Mittelstand
BEST OF 2014
INNOVATIONSPREIS-IT
Cloud Computing

7. Model Training Model
Integration

8. X Y
1 FALSE
9 TRUE
6 TRUE
4 FALSE
8 TRUE
2 FALSE
7 TRUE
3 FALSE
4 FALSE
5 FALSE
10 TRUE
15 TRUE
12 TRUE
X Y
1 FALSE
2 FALSE
3 FALSE
4 FALSE
4 FALSE
5 FALSE
6 TRUE
7 TRUE
8 TRUE
9 TRUE
10 TRUE
12 TRUE
15 TRUE
https://www.slideshare.net/damirdobric/azure-machine-learning-intro

9. https://work.caltech.edu/telecourse.html

10. X Y RESULT
----------------------
0.3 0.01 0
0.09 0.09 0
0.11 0.98 0
0.65 0.25 1
0.4 0.82 0
0.25 0.45 1
0.26 0.38 1
0.38 0.6 1
0.48 0.45 1
0.88 0.15 1
0.5 0.88 0
0.6 0.81 0

12. https://github.com/UniversityOfAppliedSciencesFrankfurt/LearningApi/tree/mlp-backpropagation/LearningApi/src/MLAlgorithms/NeuralNetworks/NeuralNet.MLPerceptron/Documentation

13. https://sud.vdma.org/viewer/-/v2article/render/26018603

14. Multilayer
Perceptron

16. • The model with 9 hidden neurons gives the best results,
with a correct result percentage of 77.5%.
• The number of hidden neurons can have a great impact
on the network performance:
• Too few hidden neurons cannot solve complex credit
rating problems,
• Too many of them may result in low efficiency and
accuracy.

21. Brauchen wir Cloud für KI?

23. https://sud.vdma.org/viewer/-/v2article/render/26018603
https://github.com/UniversityOfAppliedSciencesFrankfurt/LearningApi
https://www.slideshare.net/damirdobric/azure-machine-learning-intro

24. https://about.me/damirdobric
https://www.meetup.com/Azure-Meetup-Frankfurt/

25. © Copyright daenet GmbH. All rights reserved.