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MACHINE LEARNING FORBEGINNERS
–WHY IS DATA SCIENCE SO
COMPLICATED?
Matthias Heise, 29. January 2019
ALGORITHMPIGEONS
DATAPEAS
LABELSGOOD & BAD
THE FAIRY TALE OF
CINDERELLA
the algorithm
Deterministische Algorithmen vs. Machine Learning
„nach Regeln programmiert“ „gezeigt“ Regeln/Modell?
NLPNatural Language Processing
NLGNatural Language Generation
MLMachine Learning
AI Artifical Intelligence
DLDeep
Learning
Y
LINEAR REGRESSION
X
???
Y
SUPPORT VECTOR MACHINES (SVM)
X
??
DLDeep
Learning
the dataGarbage in – Garbage out
Y
X
Y
X
ONE‐DIMENSIONAL, NON‐LINEAR SEPARABLE
Y
X
MAPPED TO HIGHER DIMENSION, LINEAR SEPARABLE
KERNEL TRICK
Y
X
the featuresnot always obvious,
but will highly affect the algorithm
0
9
12345678
MNIST handwritten digits challenge
28 pixels
28 pixels
28 x 28 = 784 pixels at 254 grayscale… 0 55 114 254 254 10 0 0 …
… 0 0.22 0.45 1 1 0.04 0 0 …
normalized vector
corner locations + angle
X, Y, start & draw angle = 4 values
… 0 0.22 0.45 1 1 0.04 0 0 …
12 6 180 30
combine with grayscale pixels
12 6 180 30
Understanding the data
Selecting the right algorithm
Engineeringof features Model
building(can be) solved by Deep Learning
Integrate key competences for digital transformation
Big data
Science, engineering,operations
Security
Combining traditional & new security
concepts
Cloud
One management,
any cloud
21
2008*um was founded, focus on cloud and big data
25,500employees at Orange Business Services
2,200Cloud experts
The Unbelievable Machine (*um) –now part of Orange Business Services
2018Basefarm/*um acquired by Orange Business Services
2017*um acquired by Basefarm
2,400AI and data experts
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Copyright © Basefarm Group 2018 All Rights Reserved. No part of this presentation may be reproduced copied, modified or adapted, without Basefarm’s express consent.
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