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DKUM
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Title:
Uporaba globokega učenja s knjižnico Deeplearning4j na primeru prepoznave obrazov
Authors:
ID
Vrbančič, Grega
(
Author
)
ID
Podgorelec, Vili
(
Mentor
)
More about this mentor...
Files:
MAG_Vrbancic_Grega_2017.pdf
(7,06 MB)
MD5: B52311B91BD86D0FAC371C8C5AD25E13
PID:
20.500.12556/dkum/959548db-e957-4b4a-896c-67c529139281
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V magistrskem delu smo se dotaknili področja globokega učenja, spoznali smo pristope in arhitekture algoritmov globokega učenja ter jih kategorizirali v tri skupine. V nadaljevanju smo podrobneje analizirali knjižnico Deeplearning4j, predstavili osnovne funkcionalnosti ter raziskali njene možnosti za uporabo na področju globokega učenja. V praktičnem delu smo uporabo globokega učenja s knjižnico Deeplearning4j aplicirali na primeru prepoznave obrazov. Implementirali smo dve različici konvolucijskih nevronskih mrež ter dva načina učenja – lokalno ter porazdeljeno učenje.
Keywords:
strojno učenje
,
globoko učenje
,
Deeplearning4j
,
prepoznava obraza
Place of publishing:
[Maribor
Publisher:
G. Vrbančič
Year of publishing:
2017
PID:
20.500.12556/DKUM-67653
UDC:
004.932.72'1(043.2)
COBISS.SI-ID:
20969494
NUK URN:
URN:SI:UM:DK:JEAMJ5I1
Publication date in DKUM:
17.10.2017
Views:
1856
Downloads:
274
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
25.08.2017
Secondary language
Language:
English
Title:
The use of deep learning with Deeplearning4j on the case of facial recognition
Abstract:
In the master’s thesis, we briefly introduced the area of deep learning. We explored and described the approaches and architectures of deep learning algorithms and categorize them into three groups. In the following, we analyzed Deeplearning4j library, presented main features and studied possibilities of its use in the field of deep learning. In the empirical part, the use of deep learning with Deeplearning4j was applied on the case of facial recognition. We implemented two different versions of convolutional neural networks and two types of learning – local and distributed learning.
Keywords:
machine learning
,
deep learning
,
Deeplearning4j
,
facial recognition
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