Deep Learning mit Microsoft Azure:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | German |
| Veröffentlicht: |
Bonn
Rheinwerk
2019
|
| Ausgabe: | 1. Auflage |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Auf dem Cover: "Einstieg, Konzepte, Codebeispiele und Wekrzeuge, Überblick über neuronale Netze, Machine Learning, Deep Learning, Cloud-Umgebungen für Data Science und KI-Entwicklung" |
| Beschreibung: | 261 Seiten Illustrationen, Diagramme 23 cm x 17.2 cm |
| ISBN: | 9783836269933 3836269937 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV045892059 | ||
| 003 | DE-604 | ||
| 005 | 20200921 | ||
| 007 | t | ||
| 008 | 190521s2019 gw a||| |||| 00||| ger d | ||
| 015 | |a 19,N08 |2 dnb | ||
| 016 | 7 | |a 1178153568 |2 DE-101 | |
| 020 | |a 9783836269933 |c hbk. |9 978-3-8362-6993-3 | ||
| 020 | |a 3836269937 |9 3-8362-6993-7 | ||
| 035 | |a (OCoLC)1104873940 | ||
| 035 | |a (DE-599)DNB1178153568 | ||
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| 084 | |a DAT 708f |2 stub | ||
| 084 | |a DAT 303f |2 stub | ||
| 100 | 1 | |a Salvaris, Mathew |e Verfasser |0 (DE-588)1188722980 |4 aut | |
| 245 | 1 | 0 | |a Deep Learning mit Microsoft Azure |c Mathew Salvaris, Danielle Dean, Wee Hyong Tok |
| 250 | |a 1. Auflage | ||
| 264 | 1 | |a Bonn |b Rheinwerk |c 2019 | |
| 264 | 4 | |c © 2019 | |
| 300 | |a 261 Seiten |b Illustrationen, Diagramme |c 23 cm x 17.2 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Auf dem Cover: "Einstieg, Konzepte, Codebeispiele und Wekrzeuge, Überblick über neuronale Netze, Machine Learning, Deep Learning, Cloud-Umgebungen für Data Science und KI-Entwicklung" | ||
| 650 | 0 | 7 | |a Keras |g Framework, Informatik |0 (DE-588)1160521077 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Windows Azure |0 (DE-588)7693533-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Deep learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Softwareplattform |0 (DE-588)4702244-9 |2 gnd |9 rswk-swf |
| 653 | |a Tensor Flow | ||
| 653 | |a Keras | ||
| 653 | |a Künstliche Intelligenz | ||
| 653 | |a Datenanalyse | ||
| 653 | |a Predictive Analytics | ||
| 653 | |a Cognitive Services | ||
| 653 | |a Custom Vision | ||
| 653 | |a Docker | ||
| 653 | |a Visual Studio | ||
| 653 | |a VS Code | ||
| 653 | |a Azure-AI | ||
| 689 | 0 | 0 | |a Deep learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | 1 | |a Windows Azure |0 (DE-588)7693533-4 |D s |
| 689 | 0 | 2 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | 3 | |a Keras |g Framework, Informatik |0 (DE-588)1160521077 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Deep learning |0 (DE-588)1135597375 |D s |
| 689 | 1 | 1 | |a Windows Azure |0 (DE-588)7693533-4 |D s |
| 689 | 1 | 2 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 1 | 3 | |a Softwareplattform |0 (DE-588)4702244-9 |D s |
| 689 | 1 | |5 DE-604 | |
| 700 | 1 | |a Dean, Danielle |e Verfasser |0 (DE-588)1188723642 |4 aut | |
| 700 | 1 | |a Tok, Wee Hyong |e Verfasser |0 (DE-588)1188724363 |4 aut | |
| 700 | 1 | 2 | |a Salvaris, Mathew |t Deep learning with Azure |
| 710 | 2 | |a Rheinwerk Verlag |0 (DE-588)1081738405 |4 pbl | |
| 856 | 4 | 2 | |m X:MVB |q text/html |u http://deposit.dnb.de/cgi-bin/dokserv?id=0e7afbfd63a04e67814ee17f839dd58c&prov=M&dok_var=1&dok_ext=htm |3 Inhaltstext |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031275066&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-031275066 | ||
Datensatz im Suchindex
| _version_ | 1804180049363992576 |
|---|---|
| adam_text | INHALT
MATERIALIEN
ZUM
BUCH
........................................................................................................
11
UEBER
DIE
AUTOREN
...........................................................................
13
VORWORT
...............................................................................................................................
17
EINLEITUNG
............................................................................................................................
21
TEIL
I
IHR
EINSTIEG
IN
DIE
KUENSTLICHE
INTELLIGENZ
1
EINFUEHRUNG
IN
DIE
KUENSTLICHE
INTELLIGENZ
N
1.1
MICROSOFT
UND
KL
............................................................................................
29
1.2
MACHINE
LEARNING
..........................................................................................
32
1.3
DEEP
LEARNING
...............................................................................................
36
1.3.1
DER
SIEGESZUG
DES
DEEP
LEARNINGS
........................................................
39
1.3.2
ANWENDUNGSGEBIETE
DES
DEEP
LEARNINGS
.............................................
42
1.4
ZUSAMMENFASSUNG.......................................................................................
46
2
UEBERBLICK
UEBER
DEEP
LEARNING
47
2.1
ALLGEMEINE
NETZWERKSTRUKTUREN
..................................................................
48
2.1.1
CONVOLUTIONAL
NEURAL
NETWORKS
............................................................
48
2.1.2
RECURRENT
NEURAL
NETWORKS
...................................................................
51
2.1.3
GENERATIVE
ADVERSARIAL
NETWORKS
..........................................................
53
2.1.4
AUTOENCODER
...........................................................................................
54
2.2
DER
DEEP-LEARNING-WORKFLOW
......................................................................
55
2.2.1
SUCHEN
DER
RELEVANTEN
DATENSAETZE
.......................................................
56
2.2.2
VORVERARBEITEN
DER
DATENSAETZE
.............................................................
57
2.2.3
TRAINIEREN
DES
MODELLS
...........................................................................
57
2.2.4
VALIDIEREN
UND
OPTIMIEREN
DES
MODELLS
................................................
58
2.2.5
BEREITSTELLEN
DES
MODELLS
.......................................................................
59
2.2.6
DEEP-LEARNING-FRAMEWORKS
UND
BERECHNUNGEN
...................................
60
5
INHALT
2.2.7
STARTHILFE
FUER
DEEP-LEARNING-LERNEN
PER
TRANSFER
UND
ANPASSUNG
AN
FACHGEBIETE
....................................................................
63
2.2.8
MODELLBIBLIOTHEK
....................................................................................
66
2.3
ZUSAMMENFASSUNG
.........................................................................................
67
3
TRENDS
IM
DEEP
LEARNING
6S
3.1
VARIATIONEN
IN
NETZWERKARCHITEKTUREN
........................................................
69
3.1.1
RESIDUAL
NETWORKS
UND
VARIANTEN
.........................................................
70
3.1.2
DENSENET
................................................................................................
70
3.1.3
KLEINE
MODELLE,
WENIGER
PARAMETER
......................................................
70
3.1.4
KAPSELNETZWERKE
....................................................................................
71
3.1.5
OBJEKTERKENNUNG
...................................................................................
73
3.1.6
OBJEKTSEGMENTIERUNG
.............................................................................
75
3.1.7
WEITERENTWICKELTE
NETZWERKE
...............................................................
75
3.1.8
AUTOMATISIERTES
MACHINE
LEARNING
.......................................................
76
3.2
HARDWARE
.......................................................................................................
78
3.2.1
SPEZIALISIERTEM
HARDWARE
......................................................................
78
3.2.2
HARDWARE
AUF
AZURE
...............................................................................
79
3.2.3
QUANTENCOMPUTING
...............................................................................
80
3.3
GRENZEN
DES
DEEP
LEARNINGS
.........................................................................
81
3.3.1
VORSICHT
VOR
HYPES
..................................................................................
81
3.3.2
GRENZEN
DER
FAEHIGKEIT
ZUR
VERALLGEMEINERUNG
......................................
82
3.3.3
RIESIGE
DATENMENGEN
FUER
MODELLE
UND
BEZEICHNUNGEN
......................
83
3.3.4
REPRODUZIERBARE
FORSCHUNG
UND
ZUGRUNDELIEGENDE
THEORIE
...............
84
3.4
EIN
BLICK
IN
DIE
ZUKUNFT:
WAS
KOENNEN
WIR
VON
DEEP
LEARNING
ERWARTEN?
.....
85
3.4.1
ETHISCHE
UND
RECHTLICHE
FRAGEN
..............................................................
86
3.5
ZUSAMMENFASSUNG
........................................................................................
88
6
INHALT
TEIL
II
DIE
AZURE
KL-PLATTFORM
UND
IHR
WERKZEUGKASTEN
4
MICROSOFT-KL-PLATTFORM
91
4.1
DIENSTE
..........................................................................................................
93
4.1.1
VORKONFIGURIERTE
KL:
COGNITIVE
SERVICES
.................................................
93
4.1.2
GESPRAECHS-KL:
BOT
FRAMEWORK
................................................................
95
4.1.3
BENUTZERDEFINIERTE
KL:
AZURE
MACHINE
LEARNING
SERVICES
......................
95
4.1.4
BENUTZERDEFINIERTE
KL:
BATCH
AI
.............................................................
96
4.2
INFRASTRUKTUR
.................................................................................................
97
4.2.1
DATA
SCIENCE
VIRTUAL
MACHINE
.........................................................
97
4.2.2
SPARK
.......................................................................................................
99
4.2.3
HOSTEN
VON
CONTAINERN
..........................................................................
100
4.2.4
DATENSPEICHERUNG
..................................................................................
101
4.3
TOOLS
..............................................................................................................
102
4.3.1
AZURE
MACHINE
LEARNING
STUDIO
.............................................................
102
4.3.2
INTEGRIERTE
ENTWICKLUNGSUMGEBUNGEN
..................................................
103
4.3.3
DEEP-LEARNING-FRAMEWORKS
..................................................................
104
4.4
GESAMTE
AZURE-PLATTFORM
............................................................................
104
4.5
ERSTE
SCHRITTE
MIT
DER
DEEP
LEARNING
VIRTUAL
MACHINE
..................................
105
4.5.1
AUSFUEHREN
DES
NOTEBOOK-SERVERS
...........................................................
107
4.6
ZUSAMMENFASSUNG
.......................................................................................
107
5
COGNITIVE
SERVICES
UND
CUSTOM
VISION
109
5.1
VORKONFIGURIERTE
KL:
ANLASS
UND
VORGEHENSWEISE........................................
109
5.2
COGNITIVE
SERVICES
NUTZEN
............................................................................
111
5.3
VERFUEGBARE
ARTEN
VON
COGNITIVE
SERVICES
.....................................................
114
5.3.1
MASCHINELLES
SEHEN-APIS
.......................................................................
114
5.4
ERSTE
SCHRITTE
MIT
COGNITIVE
SERVICES
............................................................
121
5.5
CUSTOM
VISION
...............................................................................................
127
5.5.1
HALLO
WELT!
FUER
CUSTOM
VISION
............................................................
128
5.5.2
EXPORTIEREN
VON
CUSTOM
VISION-MODELLEN
.............................................
133
5.6
ZUSAMMENFASSUNG
.......................................................................................
134
7
INHALT
TEIL
III
KL-NETZWERKE
IN
DER
PRAXIS
6
CONVOLUTIONAL
NEURAL
NETWORKS
139
6.1
DIE
FALTUNG
IN
CONVOLUTIONAL
NEURAL
NETWORKS
............................................
140
6.1.1
FALTUNGSSCHICHT
.......................................................................................
141
6.1.2
POOLING-SCHICHT
.......................................................................................
142
6.1.3
AKTIVIERUNGSFUNKTIONEN
..........................................................................
143
6.2
CNN-ARCHITEKTUR
............................................................................................
146
6.3
TRAINIEREN
EINES
KLASSIFIZIERUNGS-CNN
..........................................................
146
6.4
GRUENDE
FUER
DIE
VERWENDUNG
VON
CNNS
.........................................................
148
6.5
TRAINIEREN
EINES
CNN
MIT
CIFAR-10...............................................................
149
6.6
TRAINING
EINES
TIEFEN
CNN
AUF
EINER
GPU
......................................................
154
6.6.1
MODELL
1
...................................................................................................
154
6.6.2
MODELL
2
...................................................................................................
155
6.6.3
MODELL
3
...................................................................................................
157
6.6.4
MODELL
4
...................................................................................................
159
6.7
TRANSFERLERNEN
................................................................................................
161
6.8
ZUSAMMENFASSUNG
........................................................................................
162
7
RECURRENT
NEURAL
NETWORKS
163
7.1
RNN-ARCHITEKTUREN
........................................................................................
166
7.2
TRAINIEREN
VON
RNNS
......................................................................................
169
7.3
GATED
RNNS
....................................................................................................
170
7.4
SEQUENZ-ZU-SEQUENZ-MODELLE
UND
AUFMERKSAMKEITSMECHANISMUS..........
172
7.5
RNN-BEISPIELE
................................................................................................
174
7.5.1
BEISPIEL
1:
STIMMUNGSANALYSE
................................................................
175
7.5.2
BEISPIEL
2:
BILDKLASSIFIZIERUNG
.................................................................
175
7.5.3
BEISPIEL
3:
ZEITREIHE
................................................................................
178
7.6
ZUSAMMENFASSUNG
........................................................................................
181
8
INHALT
8
GENERATIVE
ADVERSARIAL
NETWORKS
183
8.1
WAS
SIND
GENERATIVE
ADVERSARIAL
NETWORKS?
................................................
183
8.2
CYCLE-CONSISTENT
ADVERSARIAL
NETWORKS
.......................................................
188
8.3
DER
CYCLEGAN-CODE
.......................................................................................
190
8.4 NETZWERKARCHITEKTUR
FUER
DEN
GENERATOR
UND
DEN
DISKRIMINATOR
................
193
8.5
DEFINIEREN
DER
CYCLEGAN-KLASSE
...................................................................
197
8.6
VERLUST
DURCH
UNTERSCHIEDE
UND
ZYKLUSVERLUST............................................
198
8.7
ERGEBNISSE
.....................................................................................................
199
8.8
ZUSAMMENFASSUNG
.......................................................................................
199
TEIL
IV
KL-ARCHITEKTUREN
UND
BEST
PRACTICES
9
TRAINIEREN
VON
KL-MODELLEN
203
9.1
TRAININGSOPTIONEN
.........................................................................................
203
9.1.1
VERTEILTES
TRAINING
..................................................................................
204
9.1.2
DEEP
LEARNING
VIRTUAL
MACHINE
...............................................................
205
9.1.3
BATCH
SHIPYARD
.......................................................................................
206
9.1.4
BATCH
AI
...................................................................................................
207
9.1.5
DEEP
LEARNING
WORKSPACE
......................................................................
208
9.2
BEISPIELE
ZUR
VERANSCHAULICHUNG
..................................................................
209
9.2.1
TRAINIEREN
EINES
DNN
IN
BATCH
SHIPYARD
...............................................
209
9.2.2
AZURE
MACHINE
LEARNING
SERVICES
..........................................................
225
9.2.3
WEITERE
OPTIONEN
FUER
DAS
KL-TRAINING
AUF
AZURE
...................................
226
9.3
ZUSAMMENFASSUNG
.......................................................................................
227
10
OPERATIONALISIEREN
VON
KL-MODELLEN
22S
10.1
PLATTFORMEN
FUER
DIE
OPERATIONALISIERUNG
.......................................................
229
10.1.1
DLVM
......................................................................................................
230
10.1.2
AZURE
CONTAINER
INSTANCES
.....................................................................
231
9
INHALT
10.1.3
AZURE-WEB-APPS
.....................................................................................
232
10.1.4
AZURE
KUBERNETES
SERVICES
.....................................................................
232
10.1.5
AZURE
SERVICE
FABRIC
................................................................................
235
10.1.6
BATCH
AI
...................................................................................................
235
10.1.7
AZURE
DISTRIBUTED
DATA
ENGINEERING
TOOLKIT
(AZTK)
..............................
237
10.1.8
HDINSIGHT
UND
DATABRICKS
......................................................................
238
10.1.9
SQLSERVER
...............................................................................................
239
10.2
UEBERSICHT
UEBER
DIE
OPERATIONALISIERUNG
........................................................
239
10.3
AZURE
MACHINE
LEARNING
SERVICES
..................................................................
242
10.4
ZUSAMMENFASSUNG
........................................................................................
242
ANMERKUNGEN
......................................................................................................................
245
INDEX
....................................................................................................................................
257
10
|
| any_adam_object | 1 |
| author | Salvaris, Mathew Dean, Danielle Tok, Wee Hyong |
| author2 | Salvaris, Mathew |
| author2_role | |
| author2_variant | m s ms |
| author_GND | (DE-588)1188722980 (DE-588)1188723642 (DE-588)1188724363 |
| author_facet | Salvaris, Mathew Dean, Danielle Tok, Wee Hyong Salvaris, Mathew |
| author_role | aut aut aut |
| author_sort | Salvaris, Mathew |
| author_variant | m s ms d d dd w h t wh wht |
| building | Verbundindex |
| bvnumber | BV045892059 |
| classification_rvk | ST 200 ST 300 ST 302 ST 510 |
| classification_tum | DAT 708f DAT 303f |
| ctrlnum | (OCoLC)1104873940 (DE-599)DNB1178153568 |
| discipline | Informatik |
| edition | 1. Auflage |
| format | Book |
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| id | DE-604.BV045892059 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:29:35Z |
| institution | BVB |
| institution_GND | (DE-588)1081738405 |
| isbn | 9783836269933 3836269937 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031275066 |
| oclc_num | 1104873940 |
| open_access_boolean | |
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| owner_facet | DE-M347 DE-29T DE-573 DE-860 DE-1043 DE-91G DE-BY-TUM DE-1051 DE-1102 DE-1046 DE-11 DE-473 DE-BY-UBG |
| physical | 261 Seiten Illustrationen, Diagramme 23 cm x 17.2 cm |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Rheinwerk |
| record_format | marc |
| spelling | Salvaris, Mathew Verfasser (DE-588)1188722980 aut Deep Learning mit Microsoft Azure Mathew Salvaris, Danielle Dean, Wee Hyong Tok 1. Auflage Bonn Rheinwerk 2019 © 2019 261 Seiten Illustrationen, Diagramme 23 cm x 17.2 cm txt rdacontent n rdamedia nc rdacarrier Auf dem Cover: "Einstieg, Konzepte, Codebeispiele und Wekrzeuge, Überblick über neuronale Netze, Machine Learning, Deep Learning, Cloud-Umgebungen für Data Science und KI-Entwicklung" Keras Framework, Informatik (DE-588)1160521077 gnd rswk-swf Windows Azure (DE-588)7693533-4 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Softwareplattform (DE-588)4702244-9 gnd rswk-swf Tensor Flow Keras Künstliche Intelligenz Datenanalyse Predictive Analytics Cognitive Services Custom Vision Docker Visual Studio VS Code Azure-AI Deep learning (DE-588)1135597375 s Windows Azure (DE-588)7693533-4 s Künstliche Intelligenz (DE-588)4033447-8 s Keras Framework, Informatik (DE-588)1160521077 s DE-604 Softwareplattform (DE-588)4702244-9 s Dean, Danielle Verfasser (DE-588)1188723642 aut Tok, Wee Hyong Verfasser (DE-588)1188724363 aut Salvaris, Mathew Deep learning with Azure Rheinwerk Verlag (DE-588)1081738405 pbl X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=0e7afbfd63a04e67814ee17f839dd58c&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031275066&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Salvaris, Mathew Dean, Danielle Tok, Wee Hyong Deep Learning mit Microsoft Azure Keras Framework, Informatik (DE-588)1160521077 gnd Windows Azure (DE-588)7693533-4 gnd Deep learning (DE-588)1135597375 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Softwareplattform (DE-588)4702244-9 gnd |
| subject_GND | (DE-588)1160521077 (DE-588)7693533-4 (DE-588)1135597375 (DE-588)4033447-8 (DE-588)4702244-9 |
| title | Deep Learning mit Microsoft Azure |
| title_alt | Deep learning with Azure |
| title_auth | Deep Learning mit Microsoft Azure |
| title_exact_search | Deep Learning mit Microsoft Azure |
| title_full | Deep Learning mit Microsoft Azure Mathew Salvaris, Danielle Dean, Wee Hyong Tok |
| title_fullStr | Deep Learning mit Microsoft Azure Mathew Salvaris, Danielle Dean, Wee Hyong Tok |
| title_full_unstemmed | Deep Learning mit Microsoft Azure Mathew Salvaris, Danielle Dean, Wee Hyong Tok |
| title_short | Deep Learning mit Microsoft Azure |
| title_sort | deep learning mit microsoft azure |
| topic | Keras Framework, Informatik (DE-588)1160521077 gnd Windows Azure (DE-588)7693533-4 gnd Deep learning (DE-588)1135597375 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Softwareplattform (DE-588)4702244-9 gnd |
| topic_facet | Keras Framework, Informatik Windows Azure Deep learning Künstliche Intelligenz Softwareplattform |
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| work_keys_str_mv | AT salvarismathew deeplearningmitmicrosoftazure AT deandanielle deeplearningmitmicrosoftazure AT tokweehyong deeplearningmitmicrosoftazure AT rheinwerkverlag deeplearningmitmicrosoftazure |