The handbook of data science and AI: generate value from data with machine learning and data analytics
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Munich
Hanser
[2024]
|
| Ausgabe: | 2nd edition |
| Schlagworte: | |
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| Beschreibung: | XXIII, 845 Seiten Illustrationen, Diagramme 24,5 cm Enthält: Online-Ressource |
| ISBN: | 9781569909348 1569909342 |
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| 100 | 1 | |a Munro, Katherine |e Verfasser |0 (DE-588)1257369903 |4 aut | |
| 245 | 1 | 0 | |a The handbook of data science and AI |b generate value from data with machine learning and data analytics |c Katherina Munro, Stefan Papp, Zoltan Toth, Wolfgang Weidinger, Danko Nikolić, Barbora Antosova Vesela, Karin Bruckmüller, Annalisa Cadonna, Jana Eder, Jeannette Gorzala, Gerald A. Hahn, Georg Langs, Roxane Licandro, Christian Mata, Sean McIntyre, Mario Meir-Huber, György Móra, Manuel Pasieska, Victoria Rugli, Rania Wazir, Günther Zauner |
| 250 | |a 2nd edition | ||
| 264 | 1 | |a Munich |b Hanser |c [2024] | |
| 264 | 4 | |c © 2024 | |
| 300 | |a XXIII, 845 Seiten |b Illustrationen, Diagramme |c 24,5 cm |e Enthält: Online-Ressource | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 653 | |a Algorithmen | ||
| 653 | |a Business Intelligence | ||
| 653 | |a Data Engineering | ||
| 653 | |a Data Scientist | ||
| 653 | |a Datenanalyse | ||
| 653 | |a Datenstrategie | ||
| 653 | |a Deep Learning | ||
| 653 | |a Machine Learning | ||
| 653 | |a Statistik | ||
| 653 | |a INF2022 | ||
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| 700 | 1 | |a Toth, Zoltan |e Verfasser |0 (DE-588)1268021873 |4 aut | |
| 700 | 1 | |a Weidinger, Wolfgang |e Verfasser |0 (DE-588)1190386240 |4 aut | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035160802 | |
Datensatz im Suchindex
| _version_ | 1815710520129355776 |
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| adam_text |
TABLE
OF
CONTENTS
PREFACE
.
XXI
ACKNOWLEDGMENTS
.
.XXIII
1
INTRODUCTION
.
1
STEFAN
PAPP
1.1
ABOUT
THIS
BOOK
.
1
1.2
THE
HALFORD
GROUP
.
2
1.2.1
ALICE
HALFORD
-
CHAIRWOMAN
.
3
1.2.2
ANALYSTS
.
4
1.2.3
"
CDO
"
.
5
1.2.4
SALES
.
6
1.2.5
IT
.
7
1.2.6
SECURITY
.
8
1.2.7
PRODUCTION
LEADER
.
9
1.2.8
CUSTOMER
SERVICE
.
10
1.2.9
HR
.
11
1.2.10
CEO
.
12
1.3
INANUTSHELL
.
13
2
THE
ALPHA
AND
OMEGA
OF
AL
.
15
STEFAN
PAPP
2.1
THE
DATA
USE
CASES
.
16
2.1.1
BIAS
.
16
2.1.2
DATALITERACY
.
19
2.2
CULTURESHOCK
.
20
2.3
IDEATION
.
24
2.4
DESIGN
PROCESS
MODELS
.
25
2.4.1
DESIGN
THINKING
.
26
2.4.2
DOUBLE
DIAMOND
.
27
2.4.3
CONDUCTING
WORKSHOPS
.
28
2.5
INANUTSHELL
.
34
3
CLOUD
SERVICES
.
35
STEFAN
PAPP
3.1
INTRODUCTION
.
35
3.2
CLOUD
ESSENTIALS
.
36
3.2.1
XAAS
.
38
3.2.2
CLOUD
PROVIDERS
.
39
3.2.3
NATIVE
CLOUD
SERVICES
.
41
3.2.4
CLOUD-NATIVE
PARADIGMS
.44
3.3
INFRASTRUCTURE
AS
A
SERVICE
.
45
3.3.1
HARDWARE
.
46
3.3.2
DISTRIBUTED
SYSTEMS
.
48
3.3.3
LINUX
ESSENTIALS
FOR
DATA
PROFESSIONALS
.
51
3.3.4
INFRASTRUCTURE
AS
CODE
.
57
3.4
PLATFORM
AS
A
SERVICE
.
61
3.4.1
CLOUD
NATIVE
PAAS
SOLUTIONS
.
62
3.4.2
EXTERNAL
SOLUTIONS
.
66
3.5
SOFTWARE
AS
A
SERVICE
.
69
3.6
INANUTSHELL
.
70
4
DATA
ARCHITECTURE
.
71
ZOLTAN
C.
TOTH
AND
SEAN
MCINTYRE
4.1
OVERVIEW
.
71
4.1.1
MASLOW
'
S
HIERARCHY
OF
NEEDS
FOR
DATA
.
72
4.1.2
DATA
ARCHITECTURE
REQUIREMENTS
.
73
4.1.3
THE
STRUCTURE
OF
A
TYPICAL
DATA
ARCHITECTURE
.
74
4.1.4
ETL
(EXTRACT,
TRANSFORM,
LOAD)
.
78
4.1.5
ELT
(EXTRACT,
LOAD,
TRANSFORM)
.
79
4.1.6
ETLT
.
80
4.2
DATA
INGESTION
AND
INTEGRATION
.
80
4.2.1
DATASOURCES
.
80
4.2.2
TRADITIONAL
FILE
FORMATS
.
82
4.2.3
MODERN
FILE
FORMATS
.
84
4.2.4
WHICH
STORAGE
OPTION
TO
CHOOSE?
.
86
4.3
DATA
WAREHOUSES,
DATA
LAKES,
AND
LAKEHOUSES
.
86
4.3.1
DATA
WAREHOUSES
.
86
4.3.2
DATA
LAKES
AND
CLOUD
DATA
PLATFORMS
.
90
4.4
DATA
TRANSFORMATION
.
93
4.4.1
SOL
.
95
4.4.2
BIG
DATA
&
APACHE
SPARK
.
103
4.4.3
CLOUD
DATA
PLATFORMS
FOR
APACHE
SPARK
.
110
4.5
WORKFLOW
ORCHESTRATION
.
112
4.5.1
DAGSTER
AND
THE
MODERN
DATA
STACK
.
114
4.6
A
DATA
ARCHITECTURE
USE
CASE
.
115
4.7
INANUTSHELL
.
119
5
DATA
ENGINEERING
.
121
STEFAN
PAPP
5.1
DIFFERENTIATING
FROM
SOFTWARE
ENGINEERING
.
122
5.2
PROGRAMMING
LANGUAGES
.
123
5.2.1
CODE
OR
NO
CODE?
.
123
5.2.2
LANGUAGE
ECOSYSTEM
.
125
5.2.3
PYTHON
.
126
5.2.4
SCALA
.
129
5.3
SOFTWARE
ENGINEERING
PROCESSES
FOR
DATA
.
132
5.3.1
CONFIGURATION
MANAGEMENT
.
132
5.3.2
CI/CD
.
133
5.4
DATA
PIPELINES
.
135
5.4.1
COMMON
CHARACTERISTICS
OF
A
DATA
PIPELINE
.
135
5.4.2
DATA
PIPELINES
IN
THE
UNIFIED
DATA
ARCHITECTURE
.
136
5.5
STORAGE
OPTIONS
.
139
5.5.1
FILE
ERA
.
139
5.5.2
DATABASEERA
.
140
5.5.3
DATALAKEERA
.
142
5.5.4
SERVERLESS
ERA
.
143
5.5.5
POLYGLOT
STORAGE
.
143
5.5.6
DATAMESHERA
.
145
5.6
TOOLING
.
146
5.6.1
BATCH:
AIRFLOW
.
146
5.6.2
STREAMING:
KAFKA
.
147
5.6.3
TRANSFORMATION:
DATABRICKS
NOTEBOOKS
.
152
5.7
COMMON
CHALLENGES
.
153
5.7.1
DATA
QUALITY
AND
DIFFERENT
STANDARDS
.
154
5.7.2
SKEWEDDATA
.
155
5.7.3
STRESSED
OPERATIONAL
SYSTEMS
.
156
5.7.4
LEGACY
OPERATIONAL
SYSTEMS
.
157
5.7.5
PLATFORM
AND
INFORMATION
SECURITY
.
157
5.8
INANUTSHELL
.
157
6
DATA
GOVERNANCE
.
159
VICTORIA
RUGLI,
MARIO
MEIR-HUBER
6.1
WHY
DO
WE
NEED
DATA
GOVERNANCE?
.
159
6.1.1
SAMPLE
1:
ACHIEVING
CLARITY
WITH
DATA
GOVERNANCE
.
160
6.1.2
SAMPLE
2:
THE
(NEGATIVE)
IMPACT
OF
POOR
DATA
GOVERNANCE
.
161
6.2
THE
BUILDING
BLOCKS
OF
DATA
GOVERNANCE
.
162
6.2.1
DATA
GOVERNANCE
EXPLAINED
.
163
6.3
PEOPLE
.
165
6.3.1
DATA
OWNERSHIP
.
165
6.3.2
DATA
STEWARDSHIP
.
168
6.3.3
DATA
GOVERNANCE
BOARD
.
169
6.3.4
CHANGE
MANAGEMENT
.
170
6.4
PROCESS
.
172
6.4.1
METADATA
MANAGEMENT
.
173
6.4.2
DATA
QUALITY
MANAGEMENT
.
176
6.4.3
DATA
SECURITY
AND
PRIVACY
.
179
6.4.4
MASTER
DATA
MANAGEMENT
.
182
6.4.5
DATA
ACCESS
AND
SEARCH
.
185
6.5
TECHNOLOGY
(DATA
GOVERNANCE
TOOLS)
.
187
6.5.1
OPEN-SOURCE
TOOLS
.
187
6.5.2
CLOUD-BASED
DATA
GOVERNANCE
TOOLS
.
193
6.6
INANUTSHELL
.
197
7
MACHINE
LEARNING
OPERATIONS
(ML
OPS)
.
199
ZOLTAN.
C.
TOTH,
GYORGY
M6RA
7.1
OVERVIEW
.
199
7.1.1
SCOPEOFMLOPS
.
200
7.1.2
DATA
COLLECTION
AND
EXPLORATION
.
201
7.1.3
FEATURE
ENGINEERING
.
201
7.1.4
MODEL
TRAINING
.
201
7.1.5
MODELS
DEPLOYED
TO
PRODUCTION
.
202
7.1.6
MODEL
EVALUATION
.
202
7.1.7
MODEL
UNDERSTANDING
.
203
7.1.8
MODEL
VERSIONING
.
203
7.1.9
MODEL
MONITORING
.
204
7.2
MLOPS
IN
AN
ORGANIZATION
.
204
7.2.1
MAIN
BENEFITS
OF
MLOPS
.
204
7.2.2
CAPABILITIES
NEEDED
FOR
MLOPS
.
205
7.3
SEVERAL
COMMON
SCENARIOS
IN
THE
MLOPS
SPACE
.
205
7.3.1
INTEGRATING
NOTEBOOKS
.
205
7.3.2
FEATURES
IN
PRODUCTION
.
207
7.3.3
MODEL
DEPLOYMENT
.
209
7.3.4
MODELFORMATS
.
209
7.4
MLOPS
TOOLING
AND
MLFLOW
.
210
7.4.1
MLFLOW
.
211
7.5
INANUTSHELL
.
214
8
MACHINE
LEARNING
SECURITY
.
215
MANUEL
PASIEKA
8.1
INTRODUCTION
TO
CYBERSECURITY
.
216
8.2
ATTACK
SURFACE
.
217
8.3
ATTACK
METHODS
.
218
8.3.1
MODEL
STEALING
.
218
8.3.2
DATA
EXTRACTION
.
220
8.3.3
DATA
POISONING
.
222
8.3.4
ADVERSARIAL
ATTACK
.
225
8.3.5
BACKDOOR
ATTACK
.
227
8.4
MACHINE
LEARNING
SECURITY
OF
LARGE
LANGUAGE
MODELS
.
230
8.4.1
DATA
EXTRACTION
.
230
8.4.2
JAILBREAKING
.
232
8.4.3
PROMPT
INJECTION
.
233
8.5
AI
THREAT
MODELLING
.
236
8.6
REGULATIONS
.
237
8.7
WHERE
TO
GO
FROM
HERE
.
239
8.8
CONCLUSION
.
240
8.9
INANUTSHELL
.
241
9
MATHEMATICS
.
243
ANNALISA
CADONNA
9.1
LINEAR
ALGEBRA
.
244
9.1.1
VECTORS
AND
MATRICES
.
244
9.1.2
OPERATIONS
BETWEEN
VECTORS
AND
MATRICES
.
247
9.1.3
LINEAR
TRANSFORMATIONS
.
250
9.1.4
EIGENVALUES,
EIGENVECTORS,
AND
EIGENDECOMPOSITION
.
251
9.1.5
OTHER
MATRIX
DECOMPOSITIONS
.
252
9.2
CALCULUS
AND
OPTIMIZATION
.
253
9.2.1
DERIVATIVES
.
254
9.2.2
GRADIENT
AND
HESSIAN
.
256
9.2.3
GRADIENT
DESCENT
.
257
9.2.4
CONSTRAINED
OPTIMIZATION
.
259
9.3
PROBABILITY
THEORY
.
260
9.3.1
DISCRETE
AND
CONTINUOUS
RANDOM
VARIABLES
.
261
9.3.2
EXPECTED
VALUE,
VARIANCE,
AND
COVARIANCE
.
264
9.3.3
INDEPENDENCE,
CONDITIONAL
DISTRIBUTIONS,
AND
BAYES
'
THEOREM
.
266
9.4
INANUTSHELL
.
267
10
STATISTICS
-
BASICS
.
269
RANIA
WAZIR,
GEORG
LANGS,
ANNALISA
CADONNA
10.1
DATA
.
270
10.2
SIMPLE
LINEAR
REGRESSION
.
271
10.3
MULTIPLE
LINEAR
REGRESSION
.
278
10.4
LOGISTIC
REGRESSION
.
280
10.5
HOW
GOOD
IS
OUR
MODEL?
.
287
10.6
INANUTSHELL
.
289
11
BUSINESS
INTELLIGENCE
(BI)
.
291
CHRISTIAN
MATA
11.1
INTRODUCTION
TO
BUSINESS
INTELLIGENCE
.
293
11.1.1
DEFINITION
OF
BUSINESS
INTELLIGENCE
.
294
11.1.2
ROLE
IN
ORGANIZATIONS
.
294
11.1.3
DEVELOPMENT
OF
BUSINESS
INTELLIGENCE
.
295
11.1.4
DATA
SCIENCE
AND
AI
IN
THE
CONTEXT
OFBI
.
297
11.1.5
DATA
FOR
DECISION-MAKING
.
299
11.1.6
UNDERSTANDING
BUSINESS
CONTEXT
.
300
11.1.7
BUSINESS
INTELLIGENCE
ACTIVITIES
.
302
11.2
DATA
MANAGEMENT
FUNDAMENTALS
.
304
11.2.1
WHAT
IS
DATA
MANAGEMENT,
DATA
INTEGRATION
AND
DATA
WAREHOUSING?
.
305
11.2.2
DATA
LOAD
PROCESSES
-
THE
CASE
OF
ETL
OR
ELT.
306
11.2.3
DATAMODELING
.
308
11.3
REPORTING
AND
DATA
ANALYSIS
.
314
11.3.1
REPORTING
.
314
11.3.2
TYPES
OF
REPORTS
.
317
11.3.3
DATAANALYSIS
.
318
11.3.4
VISUAL
ANALYSIS
.
320
11.3.5
SIGNIFICANT
TRENDS
.
321
11.3.6
RELEVANT
BI
TECHNOLOGIES
.
323
11.3.7
BI
TOOL
EXAMPLES
.
326
11.4
BI
AND
DATA
SCIENCE:
COMPLEMENTARY
DISCIPLINES
.
329
11.4.1
DIFFERENCES
.
329
11.4.2
SIMILARITIES
.
330
11.4.3
INTERDEPENDENCIES
.
330
11.5
OUTLOOK
FOR
BUSINESS
INTELLIGENCE
.
331
11.5.1
EXPECTATIONS
FOR
THE
EVOLUTION
OF
BI
.
332
11.6
INANUTSHELL
.
333
12
MACHINE
LEARNING
.
335
GEORG
LANGS,
KATHERINE
MUNRO,
RANIA
WAZIR
12.1
INTRODUCTION
.
335
12.2
BASICS:
FEATURE
SPACES
.
337
12.3
CLASSIFICATION
MODELS
.
340
12.3.1
K-NEAREST-NEIGHBOR-CLASSIFIER
.
341
12.3.2
SUPPORT
VECTOR
MACHINE
.
342
12.3.3
DECISION
TREES
.
342
12.4
ENSEMBLE
METHODS
.
344
12.4.1
BIAS
AND
VARIANCE
.
345
12.4.2
BAGGING:
RANDOM
FORESTS
.
346
12.4.3
BOOSTING:
ADABOOST
.
350
12.4.4
THE
LIMITATIONS
OF
FEATURE
CONSTRUCTION
AND
SELECTION
.
350
12.5
UNSUPERVISED
LEARNING:
LEARNING
WITHOUT
LABELS
.
351
12.5.1
CLUSTERING
.
351
12.5.2
MANIFOLD
LEARNING
.
352
12.5.3
GENERATIVE
MODELS
.
353
12.6
ARTIFICIAL
NEURAL
NETWORKS
AND
DEEP
LEARNING
.
354
12.6.1
THE
PERCEPTRON
.
354
12.6.2
ARTIFICIAL
NEURAL
NETWORKS
.
355
12.6.3
DEEPLEARNING
.
357
12.6.4
CONVOLUTIONAL
NEURAL
NETWORKS
.
357
12.6.5
TRAINING
CONVOLUTIONAL
NEURAL
NETWORKS
.
358
12.6.6
RECURRENT
NEURAL
NETWORKS
.
360
12.6.7
LONG
SHORT-TERM
MEMORY
.
361
12.6.8
AUTOENCODERS
AND
U-NETS
.
363
12.6.9
ADVERSARIAL
TRAINING
APPROACHES
.
364
12.6.10
GENERATIVE
ADVERSARIAL
NETWORKS
.
365
12.6.11
CYCLE
GANS
AND
STYLE
GANS
.
367
12.7
TRANSFORMERS
AND
ATTENTION
MECHANISMS
.
368
12.7.1
THE
TRANSFORMER
ARCHITECTURE
.
368
12.7.2
WHAT
THE
ATTENTION
MECHANISM
ACCOMPLISHES
.
370
12.7.3
APPLICATIONS
OF
TRANSFORMER
MODELS
.
370
12.8
REINFORCEMENT
LEARNING
.
371
12.9
OTHER
ARCHITECTURES
AND
LEARNING
STRATEGIES
.
374
12.10
VALIDATION
STRATEGIES
FOR
MACHINE
LEARNING
TECHNIQUES
.
374
12.11
CONCLUSION
.
375
12.12
INANUTSHELL
.
376
13
BUILDING
GREAT
ARTIFICIAL
INTELLIGENCE
.
377
DANKO
NIKOLID
13.1
HOW
AI
RELATES
TO
DATA
SCIENCE
AND
MACHINE
LEARNING
.
377
13.2
A
BRIEF
HISTORY
OF
AI
.
381
13.3
FIVE
RECOMMENDATIONS
FOR
DESIGNING
AN
AI
SOLUTION
.
383
13.3.1
RECOMMENDATION
NO.
1:
BE
PRAGMATIC
.
383
13.3.2
RECOMMENDATION
NO.
2:
MAKE
IT
EASIER
FOR
MACHINES
TO
LEARN
-
CREATE
INDUCTIVE
BIASES
.
385
13.3.3
RECOMMENDATION
NO.
3:
PERFORM
ANALYTICS
.
390
13.3.4
RECOMMENDATION
NO.
4:
BEWARE
OF
THE
SCALING
TRAP
.
392
13.3.5
RECOMMENDATION
NO.
5:
BEWARE
OF
THE
GENERALITY
TRAP
(THERE
IS
NO
SUCH
A
THING
AS
FREE
LUNCH)
.
401
13.4
HUMAN-LEVEL
INTELLIGENCE
.406
13.5
INANUTSHELL
.
408
14
SIGNAL
PROCESSING
.
411
JANA
EDER
14.1
INTRODUCTION
.
411
14.2
SAMPLING
AND
QUANTIZATION
.
413
14.3
FREQUENCY
DOMAIN
ANALYSIS
.
416
14.3.1
FOURIER
TRANSFORM
.
416
14.4
NOISE
REDUCTION
AND
FILTERING
TECHNIQUES
.
422
14.4.1
DENOISING
USING
A
GAUSSIAN
LOW-PASS
FILTER
.
423
14.5
TIME
DOMAIN
ANALYSIS
.
425
14.5.1
SIGNAL
NORMALIZATION
AND
STANDARDIZATION
.
425
14.5.2
SIGNAL
TRANSFORMATION
AND
FEATURE
EXTRACTION
.
425
14.5.3
TIME
SERIES
DECOMPOSITION
TECHNIQUES
.
428
14.5.4
AUTOCORRELATION:
UNDERSTANDING
SIGNAL
SIMILARITY
OVER
TIME
.
431
14.6
TIME-FREQUENCY
DOMAIN
ANALYSIS
.434
14.6.1
SHORT
TERM
FOURIER
TRANSFORM
AND
SPECTROGRAM
.434
14.6.2
DISCRETE
WAVELET
TRANSFORM
.434
14.6.3
GRAMIAN
ANGULAR
FIELD
.
435
14.7
THE
RELATIONSHIP
OF
SIGNAL
PROCESSING
AND
MACHINE
LEARNING
.
437
14.7.1
TECHNIQUES
FOR
FEATURE
ENGINEERING
.
438
14.7.2
PREPARING
FOR
MACHINE
LEARNING
.
438
14.8
PRACTICAL
APPLICATIONS
.
439
14.9
INANUTSHELL
.
441
15
FOUNDATION
MODELS
.
443
DANKO
NIKOLIC
15.1
THE
IDEA
OF
A
FOUNDATION
MODEL
.
443
15.2
HOW
TO
TRAIN
A
FOUNDATION
MODEL?
.446
15.3
HOW
DO
WE
USE
FOUNDATION
MODELS?
.
449
15.4
A
BREAKTHROUGH:
THERE
IS
NO
END
TO
LEARNING
.
455
15.5
INANUTSHELL
.
456
16
GENERATIVE
AL
AND
LARGE
LANGUAGE
MODELS
.
459
KATHERINE
MUNRO,
GERALD
HAHN,
DANKO
NIKOLIC
16.1
INTRODUCTION
TO
"
GEN
AI
"
.
459
16.2
GENERATIVE
AI
MODALITIES
.460
16.2.1
METHODS
FOR
TRAINING
GENERATIVE
MODELS
.
462
16.3
LARGE
LANGUAGE
MODELS
.
462
16.3.1
WHAT
ARE
"LLMS"?
.
462
16.3.2
HOW
IS
SOMETHING
LIKE
CHATGPT
TRAINED?
.
464
16.3.3
METHODS
FOR
USING
LLMS
DIRECTLY
.
465
16.3.4
METHODS
FOR
CUSTOMIZING
AN
LLM
.
475
16.4
VULNERABILITIES
AND
LIMITATIONS
OF
GEN
AI
MODELS
.
483
16.4.1
INTRODUCTION
.
483
16.4.2
PROMPT
INJECTION
AND
JAILBREAKING
ATTACKS
.
484
16.4.3
HALLUCINATIONS,
CONFABULATIONS,
AND
REASONING
ERRORS
.
487
16.4.4
COPYRIGHT
CONCERNS
.
488
16.4.5
BIAS
.
491
16.5
BUILDING
ROBUST,
EFFECTIVE
GEN
AI
APPLICATIONS
.
494
16.5.1
CONTROL
STRATEGIES
THROUGHOUT
DEVELOPMENT
AND
USE
.
494
16.5.2
GUARDRAILS
.
495
16.5.3
USING
GENERATIVE
AI
SAFELY
AND
SUCCESSFULLY
.
496
16.6
INANUTSHELL
.
497
17
NATURAL
LANGUAGE
PROCESSING
(NLP)
.
503
KATHERINE
MUNRO
17.1
WHAT
IS
NLP
AND
WHY
IS
IT
SO
VALUABLE?
.
503
17.2
WHY
LEARN
"
TRADITIONAL
"
NLP
IN
THE
"
AGE
OF
LARGE
LANGUAGE
MODELS
"
?
.
505
17.3
NLP
DATA
PREPARATION
TECHNIQUES
.
506
17.3.1
THE
NLP
PIPELINE
.
506
17.3.2
CONVERTING
THE
INPUT
FORMAT
FOR
MACHINE
LEARNING
.
513
17.4
NLP
TASKS
AND
METHODS
.
514
17.4.1
RULE-BASED
(SYMBOLIC)
NLP
.
515
17.4.2
STATISTICAL
MACHINE
LEARNING
APPROACHES
.
518
17.4.3
NEURALNLP
.
527
17.4.4
TRANSFER
LEARNING
.
532
17.5
INANUTSHELL
.
543
18
COMPUTER
VISION
.
547
ROXANE
LICANDRO
18.1
WHAT
IS
COMPUTER
VISION?
.
547
18.2
A
PICTURE
PAINTS
A
THOUSAND
WORDS
.
549
18.2.1
THEHUMANEYE
.
549
18.2.2
IMAGE
ACQUISITION
PRINCIPLE
.
551
18.2.3
DIGITAL
FILE
FORMATS
.
556
18.2.4
IMAGE
COMPRESSION
.
557
18.3
I
SPY
WITH
MY
LITTLE
EYE
SOMETHING
THAT
IS
.
558
18.3.1
COMPUTATIONAL
PHOTOGRAPHY
AND
IMAGE
MANIPULATION
.
560
18.4
COMPUTER
VISION
APPLICATIONS
&
FUTURE
DIRECTIONS
.
564
18.4.1
IMAGE
RETRIEVAL
SYSTEMS
.
564
18.4.2
OBJECT
DETECTION,
CLASSIFICATION
AND
TRACKING
.
567
18.4.3
MEDICAL
COMPUTER
VISION
.
568
18.5
MAKING
HUMANS
SEE
.
571
18.6
INANUTSHELL
.
573
19
MODELLING
AND
SIMULATION
-
CREATE
YOUR
OWN
MODELS
.
577
GUNTHER
ZAUNER,
WOLFGANG
WEIDINGER,
DOMINIK
BRUNMEIR,
BENEDIKT
SPIEGEL
19.1
INTRODUCTION
.
578
19.2
GENERAL
CONSIDERATIONS
DURING
MODELING
.
579
19.3
MODELLING
TO
ANSWER
QUESTIONS
.
580
19.4
REPRODUCIBILITY
AND
MODEL
LIFECYCLE
.
581
19.4.1
THE
LIFECYCLE
OF
A
MODELLING
AND
SIMULATION
QUESTION
.
583
19.4.2
PARAMETER
AND
OUTPUT
DEFINITION
.
584
19.4.3
DOCUMENTATION
.
587
19.4.4
VERIFICATION
AND
VALIDATION
.
588
19.5
METHODS
.
591
19.5.1
ORDINARY
DIFFERENTIAL
EQUATIONS
(ODES)
.
592
19.5.2
SYSTEM
DYNAMICS
(SD)
.
593
19.5.3
DISCRETE
EVENT
SIMULATION
.
596
19.5.4
AGENT-BASED
MODELLING
.
599
19.6
MODELLING
AND
SIMULATION
EXAMPLES
.
601
19.6.1
DYNAMIC
MODELLING
OF
RAILWAY
NETWORKS
FOR
OPTIMAL
PATHFINDING
USING
AGENT-BASED
METHODS
AND
REINFORCEMENT
LEARNING
.
602
19.6.2
AGENT-BASED
COVID
MODELLING
STRATEGIES
.
604
19.6.3
DEEP
REINFORCEMENT
LEARNING
APPROACH
FOR
OPTIMAL
REPLENISHMENT
POLICY
IN
A
VMI
SETTING
.
609
19.6.4
FINDING
FEASIBLE
SOLUTIONS
FOR
A
RESOURCE-CONSTRAINED
PROJECT
SCHEDULING
PROBLEM
WITH
REINFORCEMENT
LEARNING
AND
IMPLEMENTING
A
DYNAMIC
PLANING
SCHEME
WITH
DISCRETE
EVENT
SIMULATION
.
612
19.7
SUMMARY
AND
LESSONS
LEARNED
.
616
19.8
INANUTSHELL
.
616
20
DATA
VISUALIZATION
.
621
BARBORA
ANTOSOVA
VESELA
20.1
HISTORY
.
622
20.2
WHICH
TOOLS
TO
USE
.
627
20.3
TYPES
OF
DATA
VISUALIZATIONS
.
629
20.3.1
SCATTERPLOT
.
630
20.3.2
LINECHART
.
630
20.3.3
COLUMN
AND
BAR
CHARTS
.
631
20.3.4
HISTOGRAM
.
632
20.3.5
PIE
CHART
.
633
20.3.6
BOXPLOT
.
634
20.3.7
HEATMAP
.
634
20.3.8
TREEDIAGRAM
.
635
20.3.9
OTHER
TYPES
OF
VISUALIZATIONS
.
636
20.4
SELECT
THE
RIGHT
DATA
VISUALIZATION
.
636
20.5
TIPS
AND
TRICKS
.
638
20.6
PRESENTATION
OF
DATA
VISUALIZATION
.
643
20.7
INANUTSHELL
.
643
21
DATA
DRIVEN
ENTERPRISES
.
647
MARIO
MEIR-HUBER,
STEFAN
PAPP
21.1
THE
THREE
LEVELS
OF
A
DATA
DRIVEN
ENTERPRISE
.
648
21.2
CULTURE
.
648
21.2.1
CORPORATE
STRATEGY
FOR
DATA
.
649
21.2.2
THE
CURRENT
STATE
ANALYSIS
.
651
21.2.3
CULTURE
AND
ORGANIZATION
OF
A
SUCCESSFUL
DATA
ORGANISATION
.
653
21.2.4
CORE
PROBLEM:
THE
SKILLS
GAP
.
660
21.3
TECHNOLOGY
.
662
21.3.1
THE
IMPACT
OF
OPEN
SOURCE
.
662
21.3.2
CLOUD
.
662
21.3.3
VENDOR
SELECTION
.
663
21.3.4
DATA
LAKE
FROM
A
BUSINESS
PERSPECTIVE
.
663
21.3.5
THEROLEOFIT
.
.
664
21.3.6
DATA
SCIENCE
LABS
.
664
21.3.7
REVOLUTION
IN
ARCHITECTURE:
THE
DATA
MESH
.
665
21.4
BUSINESS
.
667
21.4.1
BUY
AND
SHARE
DATA
.
667
21.4.2
ANALYTICAL
USE
CASE
IMPLEMENTATION
.
668
21.4.3
SELF-SERVICE
ANALYTICS
.
669
21.5
INANUTSHELL
.
669
22
CREATING
HIGH-PERFORMING
TEAMS
.
671
STEFAN
PAPP
22.1
FORMING
.
671
22.2
STORMING
.
672
22.2.1
SCENARIO:
50
SHADES
OF
RED
.
672
22.2.2
SCENARIO:
RETROSPECTIVE
.676
22.3
NORMING
.
678
22.3.1
CHANGE
MANAGEMENT
AND
TRANSITION
.
678
22.3.2
RACI
MATRIX
.
680
22.3.3
SMART
.
682
22.3.4
AGILE
PROCESSES
.
683
22.3.5
COMMUNICATION
CULTURE
.
685
22.3.6
DATAOPS
.
686
22.4
PERFORMING
.
690
22.4.1
SCENARIO:
A
NEW
DAWN
.
691
22.4.2
GROWTH
MINDSETS
.
692
22.5
INANUTSHELL
.
695
23
ARTIFICIAL
INTELLIGENCE
ACT
.
697
JEANNETTE
GORZALA,
KARIN
BRUCKMULLER
23.1
INTRODUCTION
.
698
23.2
DEFINITION
OF
AI
SYSTEMS
.
700
23.3
SCOPE
AND
PURPOSE
OF
THE
AI
ACT
.
701
23.3.1
THE
RISK-BASED
APPROACH
.
702
23.3.2
UNACCEPTABLE
RISK
AND
PROHIBITED
AI
PRACTICES
.
703
23.3.3
HIGH-RISK
AI
SYSTEMS
AND
COMPLIANCE
.
705
23.3.4
MEDIUM
RISK
AND
TRANSPARENCY
OBLIGATIONS
.
707
23.3.5
MINIMAL
RISK
AND
VOLUNTARY
COMMITMENTS
.
708
23.4
GENERAL
PURPOSE
AI
MODELS
.
708
23.5
TIMELINE
AND
APPLICABILITY
.
711
23.6
PENALTIES
.
711
23.7
AI
AND
CIVIL
LIABILITY
.
712
23.8
AI
AND
CRIMINAL
LIABILITY
.
712
23.9
INANUTSHELL
.
715
24
AL
IN
DIFFERENT
INDUSTRIES
.
717
STEFAN
PAPP,
MARIO
MEIR-HUBER,
WOLFGANG
WEIDINGER,
THOMAS
TREML
24.1
AUTOMOTIVE
.
720
24.1.1
VISION
.
721
24.1.2
DATA
.
722
24.1.3
USECASES
.
722
24.1.4
CHALLENGES
.
723
24.2
AVIATION
.
725
24.2.1
VISION
.
725
24.2.2
DATA
.
726
24.2.3
USECASES
.
726
24.2.4
CHALLENGES
.
727
24.3
ENERGY
.
727
24.3.1
VISION
.
728
24.3.2
DATA
.
728
24.3.3
USECASES
.
729
24.3.4
CHALLENGES
.
730
24.4
FINANCE
.
730
24.4.1
VISION
.
730
24.4.2
DATA
.
731
24.4.3
USECASES
.
731
24.4.4
CHALLENGES
.
733
24.5
HEALTH
.
733
24.5.1
VISION
.
734
24.5.2
DATA
.
735
24.5.3
USECASES
.
735
24.5.4
CHALLENGES
.
735
24.6
GOVERNMENT
.
736
24.6.1
VISION
.
736
24.6.2
DATA
.
737
24.6.3
USECASES
.
737
24.6.4
CHALLENGES.
.
740
24.7
ART
.
740
24.7.1
VISION
.
741
24.7.2
DATA
.
741
24.7.3
USECASES
.
741
24.7.4
CHALLENGES
.
742
24.8
MANUFACTURING
.
742
24.8.1
VISION
.
743
24.8.2
DATA
.
743
24.8.3
USECASES
.
743
24.8.4
CHALLENGES
.
744
24.9
OILANDGAS
.
745
24.9.1
VISION
.
745
24.9.2
DATA
.
745
24.9.3
USECASES
.
746
24.9.4
CHALLENGES
.
748
24.10
RETAIL
.
748
24.10.1
VISION
.
748
24.10.2
DATA
.
749
24.10.3
USECASES
.
749
24.10.4
CHALLENGES
.
750
24.11
TELECOMMUNICATIONS
PROVIDER
.
750
24.11.1
VISION
.
751
24.11.2
DATA
.
751
24.11.3
USE
CASES
.
751
24.11.4
CHALLENGES
.
753
24.12
TRANSPORT
.
753
24.12.1
VISION
.754
24.12.2
DATA
.754
24.12.3
USE
CASES
.754
24.12.4
CHALLENGES
.
755
24.13
TEACHING
AND
TRAINING
.
755
24.13.1
VISION
.
756
24.13.2
DATA
.
757
24.13.3
USECASES
.
757
24.13.4
CHALLENGES
.
758
24.14
THE
DIGITAL
SOCIETY
.
758
24.15
IN
A
NUTSHELL
.
760
25
CLIMATE
CHANGE
AND
AL
.
761
STEFAN
PAPP
25.1
INTRODUCTION
.
761
25.2
AI
-
A
CLIMATE
SAVER?
.
763
25.3
MEASURING
AND
REDUCING
EMISSIONS
.
763
25.3.1
BASELINE
.
763
25.3.2
DATAUSECASES
.
765
25.4
SEQUESTRATION
.
766
25.4.1
BIOLOGICAL
SEQUESTRATION
.
768
25.4.2
GEOLOGICAL
SEQUESTRATION
.
769
25.5
PREPARE
FOR
IMPACT
.
770
25.6
GEOENGINEERING
.
771
25.7
GREENWASHING
.
773
25.8
OUTLOOK
.
774
25.9
INANUTSHELL
.
776
26
MINDSET
AND
COMMUNITY
.
777
STEFAN
PAPP
26.1
DATA-DRIVEN
MINDSET
.
777
26.2
DATA
SCIENCE
CULTURE
.
780
26.2.1
START-UP
OR
CONSULTING
FIRM?
.
780
26.2.2
LABS
INSTEAD
OF
CORPORATE
POLICY
.
781
26.2.3
KEIRETSU
INSTEAD
OF
LONE
WOLF
.
781
26.2.4
AGILE
SOFTWARE
DEVELOPMENT
.
783
26.2.5
COMPANY
AND
WORK
CULTURE
.
783
26.3
ANTIPATTERNS
.
786
26.3.1
DEVALUATION
OF
DOMAIN
EXPERTISE
.
786
26.3.2
IT
WILL
TAKE
CARE
OF
IT
.
787
26.3.3
RESISTANCE
TO
CHANGE
.
787
26.3.4
KNOW-IT-ALL
MENTALITY
.
788
26.3.5
DOOMANDGLOOM
.
789
26.3.6
PENNY-PINCHING
.
789
26.3.7
FEARCULTURE
.
790
26.3.8
CONTROL
OVER
RESOURCES
.
790
26.3.9
BLIND
FAITH
IN
RESOURCES
.
791
26.3.10
THE
SWISS
ARMY
KNIFE
.
792
26.3.11
OVER-ENGINEERING
.
792
26.4
INANUTSHELL
.
793
27
TRUSTWORTHY
AL
.
795
RANIA
WAZIR
27.1
LEGAL
AND
SOFT-LAW
FRAMEWORK
.
796
27.1.1
STANDARDS
.
798
27.1.2
REGULATIONS
.
798
27.2
AI
STAKEHOLDERS
.
800
27.3
FAIRNESSINAI
.
801
27.3.1
BIAS
.
802
27.3.2
FAIRNESS
METRICS
.
805
27.3.3
MITIGATING
UNWANTED
BIAS
IN
AI
SYSTEMS
.
808
27.4
TRANSPARENCY
OF
AI
SYSTEMS
.
809
27.4.1
DOCUMENTING
THE
DATA
.
810
27.4.2
DOCUMENTING
THE
MODEL
.
811
27.4.3
EXPLAINABILITY
.
812
27.5
CONCLUSION
.
814
27.6
INANUTSHELL
.
814
28
EPILOGUE
.
815
STEFAN
PAPP
28.1
HALFORD2.0
.
815
28.1.1
ENVIRONMENTAL,
SOCIAL
AND
GOVERNANCE
.
816
28.1.2
HR
.
817
28.1.3
CUSTOMER
SATISFACTION
.
818
28.1.4
PRODUCTION
.
819
28.1.5
IT
.
820
28.1.6
STRATEGY
.
822
28.2
FINAL
WORDS
.
823
28.3
INANUTSHELL
.
824
29
THE
AUTHORS
.825
INDEX
.
833 |
| any_adam_object | 1 |
| author | Munro, Katherine Papp, Stefan Toth, Zoltan Weidinger, Wolfgang |
| author_GND | (DE-588)1257369903 (DE-588)1161133895 (DE-588)1268021873 (DE-588)1190386240 |
| author_facet | Munro, Katherine Papp, Stefan Toth, Zoltan Weidinger, Wolfgang |
| author_role | aut aut aut aut |
| author_sort | Munro, Katherine |
| author_variant | k m km s p sp z t zt w w ww |
| building | Verbundindex |
| bvnumber | BV049820603 |
| classification_rvk | ST 302 |
| classification_tum | DAT 700 |
| ctrlnum | (OCoLC)1454745073 (DE-599)BVBBV049820603 |
| dewey-full | 005.7 006.312 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security 006 - Special computer methods |
| dewey-raw | 005.7 006.312 |
| dewey-search | 005.7 006.312 |
| dewey-sort | 15.7 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 2nd edition |
| format | Book |
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| id | DE-604.BV049820603 |
| illustrated | Illustrated |
| indexdate | 2024-11-14T15:01:28Z |
| institution | BVB |
| institution_GND | (DE-588)1064064051 |
| isbn | 9781569909348 1569909342 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035160802 |
| oclc_num | 1454745073 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-12 DE-860 DE-210 |
| owner_facet | DE-91 DE-BY-TUM DE-12 DE-860 DE-210 |
| physical | XXIII, 845 Seiten Illustrationen, Diagramme 24,5 cm Enthält: Online-Ressource |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Hanser |
| record_format | marc |
| spelling | Munro, Katherine Verfasser (DE-588)1257369903 aut The handbook of data science and AI generate value from data with machine learning and data analytics Katherina Munro, Stefan Papp, Zoltan Toth, Wolfgang Weidinger, Danko Nikolić, Barbora Antosova Vesela, Karin Bruckmüller, Annalisa Cadonna, Jana Eder, Jeannette Gorzala, Gerald A. Hahn, Georg Langs, Roxane Licandro, Christian Mata, Sean McIntyre, Mario Meir-Huber, György Móra, Manuel Pasieska, Victoria Rugli, Rania Wazir, Günther Zauner 2nd edition Munich Hanser [2024] © 2024 XXIII, 845 Seiten Illustrationen, Diagramme 24,5 cm Enthält: Online-Ressource txt rdacontent n rdamedia nc rdacarrier Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Algorithmen Business Intelligence Data Engineering Data Scientist Datenanalyse Datenstrategie Deep Learning Machine Learning Statistik INF2022 Data Science (DE-588)1140936166 s Big Data (DE-588)4802620-7 s Künstliche Intelligenz (DE-588)4033447-8 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Papp, Stefan Verfasser (DE-588)1161133895 aut Toth, Zoltan Verfasser (DE-588)1268021873 aut Weidinger, Wolfgang Verfasser (DE-588)1190386240 aut Hanser Publications (DE-588)1064064051 pbl Erscheint auch als Online-Ausgabe, PDF 978-1-56990-235-6 (DE-604)BV049699431 Erscheint auch als Online-Ausgabe, ePub 978-1-56990-411-4 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035160802&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20211208 DE-101 https://d-nb.info/provenance/plan#vlb 2\p vlb 20211208 DE-101 https://d-nb.info/provenance/plan#vlb 3\p vlb 20211208 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Munro, Katherine Papp, Stefan Toth, Zoltan Weidinger, Wolfgang The handbook of data science and AI generate value from data with machine learning and data analytics Künstliche Intelligenz (DE-588)4033447-8 gnd Big Data (DE-588)4802620-7 gnd Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4802620-7 (DE-588)1140936166 (DE-588)4193754-5 |
| title | The handbook of data science and AI generate value from data with machine learning and data analytics |
| title_auth | The handbook of data science and AI generate value from data with machine learning and data analytics |
| title_exact_search | The handbook of data science and AI generate value from data with machine learning and data analytics |
| title_full | The handbook of data science and AI generate value from data with machine learning and data analytics Katherina Munro, Stefan Papp, Zoltan Toth, Wolfgang Weidinger, Danko Nikolić, Barbora Antosova Vesela, Karin Bruckmüller, Annalisa Cadonna, Jana Eder, Jeannette Gorzala, Gerald A. Hahn, Georg Langs, Roxane Licandro, Christian Mata, Sean McIntyre, Mario Meir-Huber, György Móra, Manuel Pasieska, Victoria Rugli, Rania Wazir, Günther Zauner |
| title_fullStr | The handbook of data science and AI generate value from data with machine learning and data analytics Katherina Munro, Stefan Papp, Zoltan Toth, Wolfgang Weidinger, Danko Nikolić, Barbora Antosova Vesela, Karin Bruckmüller, Annalisa Cadonna, Jana Eder, Jeannette Gorzala, Gerald A. Hahn, Georg Langs, Roxane Licandro, Christian Mata, Sean McIntyre, Mario Meir-Huber, György Móra, Manuel Pasieska, Victoria Rugli, Rania Wazir, Günther Zauner |
| title_full_unstemmed | The handbook of data science and AI generate value from data with machine learning and data analytics Katherina Munro, Stefan Papp, Zoltan Toth, Wolfgang Weidinger, Danko Nikolić, Barbora Antosova Vesela, Karin Bruckmüller, Annalisa Cadonna, Jana Eder, Jeannette Gorzala, Gerald A. Hahn, Georg Langs, Roxane Licandro, Christian Mata, Sean McIntyre, Mario Meir-Huber, György Móra, Manuel Pasieska, Victoria Rugli, Rania Wazir, Günther Zauner |
| title_short | The handbook of data science and AI |
| title_sort | the handbook of data science and ai generate value from data with machine learning and data analytics |
| title_sub | generate value from data with machine learning and data analytics |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Big Data (DE-588)4802620-7 gnd Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Künstliche Intelligenz Big Data Data Science Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035160802&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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