The ultimate data and AI guide: 150 FAQs about artificial intelligence, machine learning and data
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| Sprache: | English |
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München
Data AI Press
[2020]
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxviii, 371 Seiten Illustrationen, Diagramme 23.4 cm x 15.6 cm |
| ISBN: | 9783982173702 3982173701 |
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MARC
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| 020 | |a 9783982173702 |c : EUR 23.99 (DE) (freier Preis), EUR 24.70 (AT) (freier Preis) |9 978-3-9821737-0-2 | ||
| 020 | |a 3982173701 |9 3-9821737-0-1 | ||
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| 035 | |a (OCoLC)1151743844 | ||
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| 100 | 1 | |a Thamm, Alexander |d 1982- |e Verfasser |0 (DE-588)1225376793 |4 aut | |
| 245 | 1 | 0 | |a The ultimate data and AI guide |b 150 FAQs about artificial intelligence, machine learning and data |c Alexander Thamm, Michael Gramlich, Dr. Alexander Borek |
| 264 | 1 | |a München |b Data AI Press |c [2020] | |
| 264 | 4 | |c © 2020 | |
| 300 | |a xxviii, 371 Seiten |b Illustrationen, Diagramme |c 23.4 cm x 15.6 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 653 | |a Maschinelles Lernen | ||
| 653 | |a Machine Learning | ||
| 653 | |a Artificial Intelligence | ||
| 653 | |a Künstliche Intelligenz | ||
| 653 | |a DSGVO | ||
| 653 | |a Data Science | ||
| 653 | |a Datenbank | ||
| 653 | |a Big Data | ||
| 653 | |a Cloud | ||
| 653 | |a Daten | ||
| 653 | |a Analytics | ||
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Gramlich, Michael |e Verfasser |4 aut | |
| 700 | 1 | |a Borek, Alexander |e Verfasser |0 (DE-588)1023903156 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-9821737-1-9 |
| 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=032351329&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
Datensatz im Suchindex
| _version_ | 1805069472669630464 |
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| adam_text |
TABLE
OF
CONTENTS
FOREWORD
.
XVII
PREFACE
.
XVIII
ACKNOWLEDGMENTS
.
XXI
INTRODUCTION
.
XXII
PART
11
WHY
DO
WE
CARE:
THE
DIGITAL
TRANSFORMATION
TRAIN
1
DIGITAL
TRANSFORMATION:
THE
ROLE
OF
DATA
AND
ARTIFICIAL
INTELLIGENCE.
2
1_1
DIGITAL
TRANSFORMATION
.
2
I
I
WHAT
IS
DIGITAL
TRANSFORMATION?
.
2
2
1
WHAT
IS
THE
IMPACT
OF
DIGITAL
TRANSFORMATION
ON
COMPANIES
AND
SOCIETY?
.
4
3
1
WHAT
ARE
THE
DRIVERS
OF
DIGITAL
TRANSFORMATION?
.
7
1
2
THE
ROLE
OF
DATA
AND
AL
IN
DIGITAL
TRANSFORMATION
.
8
4
1
AL
-
WHY
IS
IT
THE
ENGINE
OF
DIGITAL
TRANSFORMATION?
.
8
5
1
DATA
-
WHY
IS
IT
THE
FUEL
OF
DIGITAL
TRANSFORMATION?
.
9
6
1
HOW
ARE
DATA
AND
AL
APPLIED
TO
GENERATE
VALUE
ACROSS
INDUSTRIES?
.
10
1_3
BUZZWORDS
IN
DIGITAL
TRANSFORMATION,
DATA
AND
AL
.
13
7
1
WHAT
IS
AN
OVERVIEW
OF
BUZZWORDS
IN
DATA
AND
AL?
.
13
8
1
WHAT
IS
THE
LOT
AND
WHAT
DOES
IT
HAVE
TO
DO
WITH
BIG
DATA?
.
14
9
1
WHAT
ARE
DATA
LAKES,
DATA
WAREHOUSES,
DATA
ARCHITECTURES,
HADOOP
AND
NOSQL
DATABASES?
.
15
10
1
WHAT
ARE
DATA
GOVERNANCE
AND
DATA
DEMOCRATIZATION?
.
16
II
I
WHAT
IS
THE
CLOUD?
.
16
12
I
WHAT
ARE
DATA
SCIENCE,
DATA
ANALYTICS,
BUSINESS
INTELLIGENCE,
DATA
MINING
AND
PREDICTIVE
ANALYTICS?
.
17
VLI
13
1
WHAT
ARE
MACHINE
LEARNING,
NEURAL
NETWORKS
AND
DEEP
LEARNING?
.
19
14
1
WHAT
ARE
AL,
NATURAL
LANGUAGE
PROCESSING,
COMPUTER
VISION
AND
ROBOTICS?
.
20
PART
III
THE
FUEL:
DATA
2
UNDERSTANDING
DATA:
THE
FUEL
OF
DIGITAL
AND
ARTIFICIAL
INTELLIGENCE
TRANSFORMATION
.
22
2_1
UNDERSTANDING
DATA
.
22
15
1
WHAT
IS
DATA?
.
22
16
1
WHY
COLLECT
DATA
AND
WHAT
ARE
THE
DIFFERENT
TYPES
OF
DATA
ANALYTICS?
.
28
17
1
HOW
IS
DATA
CREATED?
.
30
18
I
WHAT
ARE
THE
FACTORS
THAT
HAVE
ENABLED
AN
ERA
OF
MASS
DATA
CREATION
AND
STORAGE?
.
32
19
1
WHAT
IS
DATA
QUALITY
AND
WHAT
KIND
OF
DATA
QUALITY
ISSUES
ARE
THERE?
.
36
20
1
HOW
MUCH
DATA
QUALITY
DO
YOU
NEED?
.
39
2_2
TYPES
OF
DATA
.
41
21
I
WHAT
ARE
UNSTRUCTURED,
SEMI-STRUCTURED
AND
STRUCTURED
DATA?
.
41
22
1
WHAT
ARE
MASTER
DATA
AND
TRANSACTIONAL
DATA?
.
45
23
1
WHAT
IS
STREAMING
DATA
AND
WHAT
IS
THE
DIFFERENCE
BETWEEN
BATCH
AND
STREAMING
PROCESSING?
.
46
24
1
WHAT
IS
BIG
DATA?
.
48
3
DATA
STORAGE
TECHNOLOGIES
.
52
3_1
UNDERSTANDING
DATA
STORAGE
.
52
25
I
WHY
CAN
*
T
A
COMPANY
STORE
ITS
STRUCTURED
DATA
IN
AN
EXCEL
FILE
LIKE
WE
DO
ON
PCS?
.
52
26
1
WHAT
IS
A
DATABASE
AND
HOW
DOES
IT
WORK?
.
54
27
1
WHAT
ARE
THE
ADVANTAGES
OF
STORING
DATA
IN
A
DATABASE?
.
56
VIII
28
1
WHAT
TYPES
OF
DATABASES
ARE
THERE
AND
HOW
ARE
THEY
CLASSIFIED?
.
57
3_2
RELATIONAL
(SQL)
DATABASES
.
59
29
1
WHAT
IS
A
RELATIONAL
DATABASE
SYSTEM
AND
HOW
DOES
IT
WORK?
.
59
30
1
HOW
DOES
THE
RELATIONAL
MODEL
WORK?
.
61
31
I
WHAT
IS
A
KEY
ATTRIBUTE
AND
WHY
IS
IT
INDISPENSABLE?
.
64
32
1
HOW
IS
DATA
ACCESSED
AND
MANIPULATED
IN
A
RELATIONAL
DATABASE
SYSTEM
(SQL)?
.
68
33
1
WHAT
ARE
THE
STRENGTHS
OF
RELATIONAL
DATABASE
SYSTEMS?
.
72
34
1
WHAT
ARE
THE
LIMITATIONS
OF
RELATIONAL
DATABASE
SYSTEMS
AND
HOW
WERE
THEY
REVEALED
WITH
THE
DAWN
OF
BIG
DATA?
.
73
3_3
DISTRIBUTED
FILE
SYSTEMS
AND
NON-RELATIONAL
(NOSQL)
DATABASES
.
76
35
1
WHAT
ARE
COMPUTER
CLUSTERS
AND
HOW
DID
THE
IDEA
OF
*
SCALING
OUT"
FORM
THE
BASIS
FOR
STORING
AND
PROCESSING
BIG
DATA?
.
76
36
1
WHAT
ARE
DISTRIBUTED
FILE
SYSTEMS
AND
HOW
DO
WE
STORE
DATA
WITH
THEM?
.
79
37
I
WHAT
ARE
NON-RELATIONAL
(NOSQL)
DATABASES
AND
WHAT
DOES
THE
CAP
THEOREM
HAVE
TO
DO
WITH
THEM?
.
81
38
1
HOW
DO
RELATIONAL
AND
NON-RELATIONAL
DATABASES
COMPARE
AND
WHEN
IS
IT
BEST
TO
USE
EACH
ONE?
.
88
3_4
POPULAR
DATA
STORAGE
TECHNOLOGIES
.
90
39
1
WHAT
ARE
THE
TYPES
OF
DATA
STORAGE
TECHNOLOGIES?
.
90
40
1
WHAT
ARE
HADOOP
AND
THE
HADOOP
ECOSYSTEM
(E.G.
HIVE,
HBASE,
FLUME,
KAFKA)?
.
91
41
I
WHAT
IS
SPARK?
.
93
42
1
WHAT
ARE
MYSQL,
POSTGRESQL,
ORACLE,
MICROSOFT
SQL
SERVER,
SAP
HANA,
IBM
DB2
AND
TERADATA
DATABASE?
.
94
IX
43
1
WHAT
ARE
MONGODB,
NEO4J,
AMAZON
DYNAMODB,
COUCHDB
AND
REDIS?
.
95
4
ARCHITECTING
DATA:
DATA
WAREHOUSES,
DATA
LAKES
AND
THE
CLOUD
.
96
4_1
UNDERSTANDING
DATA
ARCHITECTURES
.
96
44
1
WHAT
IS
A
DATA
ARCHITECTURE
AND
WHY
DO
COMPANIES
NEED
IT?
.
96
45
1
WHAT
ARE
THE
MOST
POPULAR
ARCHITECTURAL
BLUEPRINTS?
.
99
4_2
DATA
WAREHOUSE
ARCHITECTURES
.
100
46
1
WHAT
IS
A
DATA
WAREHOUSE
(DWH)
ARCHITECTURE?
.
100
47
1
HOW
DOES
A
DWH
WORK?
.
103
48
1
WHAT
DOES
A
TYPICAL
DATA
PIPELINE
IN
A
DWH
LOOK
LIKE?
.
107
49
I
WHAT
ARE
THE
LIMITATIONS
OF
A
DWH?
.
108
50
1
WHAT
ARE
POPULAR
ETL
TOOLS?
.
110
4_3
DATA
LAKES
AND
STREAMING
ARCHITECTURES
.
111
51
I
WHAT
IS
A
DATA
LAKE
ARCHITECTURE?
.
111
52
1
HOW
DOES
A
DATA
LAKE
WORK
AND
WHERE
SHOULD
IT
BE
USED?
.
112
53
1
HOW
DO
A
DWH
AND
DATA
LAKE
COMPARE?
.
115
4_4
CLOUD
ARCHITECTURES
.
118
54
1
WHAT
IS
THE
CLOUD?
.
118
55
1
WHAT
TYPES
OF
CLOUD
ARCHITECTURES
ARE
THERE?
.
120
56
1
WHAT
TYPES
OF
CLOUD
SERVICES
ARE
THERE?
.
123
57
1
WHAT
ARE
THE
ADVANTAGES
AND
DISADVANTAGES
OF
USING
CLOUD
SERVICES?
.
126
58
1
WHAT
IS
A
SERVERLESS
ARCHITECTURE?
.
130
59
I
WHAT
ARE
THE
POPULAR
CLOUD
PROVIDERS
AND
SERVICES?
.
132
5
MANAGING
DATA
IN
A
COMPANY
.
134
5_1
PEOPLE
AND
JOB
ROLES
.
134
60
1
WHAT
DOES
A
CHIEF
DATA
AND
ANALYTICS
OFFICER
DO?
.
134
61
I
WHAT
DOES
A
DATA
ARCHITECT
DO?
.
135
62
1
WHAT
DOES
A
DATABASE
ADMINISTRATOR
DO?
.
136
X
63
1
WHAT
OTHER
JOB
ROLES
ARE
INVOLVED
IN
CREATING
AND
MAINTAINING
A
DATA
ARCHITECTURE?
.
136
5_2
DATA
GOVERNANCE
AND
DEMOCRATIZATION
.
137
64
1
WHAT
ARE
DATA
GOVERNANCE
AND
DEMOCRATIZATION
AND
WHY
DOES
DATA
NEED
TO
BE
GOVERNED
AND
DEMOCRATIZED?
.
137
65
1
WHAT
ARE
THE
KEY
ELEMENTS
OF
DATA
GOVERNANCE
AND
DATA
DEMOCRATIZATION?
.
138
66
1
HOW
CAN
WE
MAKE
DATA
MORE
FINDABLE
AND
ACCESSIBLE?
.
139
67
1
HOW
CAN
WE
MAKE
DATA
MORE
UNDERSTANDABLE
AND
SHARE
KNOWLEDGE
ON
DATA?
.
140
68
1
HOW
CAN
WE
MAKE
DATA
MORE
TRUSTWORTHY
AND
IMPROVE
THE
QUALITY
OF
DATA?
.
141
69
1
HOW
CAN
WE
EMPOWER
THE
DATA
USER
WITH
SELF-SERVICE
BL
AND
ANALYTICS?
.
141
70
1
HOW
CAN
DATA
GOVERNANCE
AND
DATA
DEMOCRATIZATION
BE
IMPLEMENTED?
.
142
5_3
DATA
SECURITY
AND
PROTECTION
(PRIVACY)
.
143
71
I
WHAT
IS
AN
OVERVIEW
OF
DATA
SECURITY,
DATA
PROTECTION
AND
DATA
PRIVACY
AND
HOW
DO
THEY
RELATE
TO
EACH
OTHER?
.
143
72
1
WHAT
IS
DATA
SECURITY
AND
HOW
CAN
IT
BE
ACHIEVED?
.
145
73
1
WHAT
IS
PERSONAL
DATA?
.
148
74
1
WHAT
IS
DATA
PROTECTION
(PRIVACY)
AND
WHY
IS
THE
DISTINCTION
BETWEEN
NON-PERSONAL
AND
PERSONAL
DATA
SO
IMPORTANT?
.
150
75
1
GENERAL
DATA
PROTECTION
REGULATION
(GDPR)
-
WHO,
WHAT,
WHERE
AND
WHY?
.
153
PART
III
I
THE
ENGINE:
ARTIFICIAL
INTELLIGENCE
AND
MACHINE
LEARNING
6
UNDERSTANDING
MACHINE
LEARNING
AS
THE
KEY
DRIVER
BEHIND
ARTIFICIAL
INTELLIGENCE
.
160
6_1
UNDERSTANDING
AL
AND
ML
.
160
76
1
WHAT
IS
AL?
.
160
XI
77
1
WHERE
CAN
AL
BE
APPLIED
AND
HOW
HAVE
APPROACHES
TO
CREATE
AL
DEVELOPED
OVER
TIME?
.
163
78
1
WHAT
IS
CURRENTLY
POSSIBLE
WITH
AL
AND
WHAT
ARE
SOME
TOP
BREAKTHROUGHS?
.
165
79
1
WHY
IS
AL
ALMOST
TANTAMOUNT
TO
ML
(AL
=
ML
+
X)
TODAY?
.
168
80
1
WHAT
IS
ML
AND
HOW
CAN
IT
CREATE
AL?
.
170
81
I
HOW
IS
A
MACHINE
ABLE
TO
LEARN
AND
WHY
IS
ML
OFTEN
CONSIDERED
*
SOFTWARE
2.0
*
?
.
172
82
1
WHAT
IS
A
MACHINE
ABLE
TO
LEAM
-
CAN
IT
PREDICT
THE
FUTURE?
.
176
6_2
TYPES
OF
ML
.
180
83
1
WHAT
TYPES
OF
ML
ARE
THERE
AND
HOW
DO
THEY
DIFFER?
.
180
84
1
WHAT
IS
SUPERVISED
ML?
.
184
85
1
WHAT
IS
THE
DIFFERENCE
BETWEEN
REGRESSION
AND
CLASSIFICATION?
.
186
86
1
WHAT
IS
UNSUPERVISED
ML?
.
187
87
1
WHAT
ARE
THE
MOST COMMONLY
USED
METHODS
IN
UNSUPERVISED
LEARNING?
.
189
88
1
WHAT
IS
REINFORCEMENT
LEARNING?
.
193
6_3
POPULAR
ML
TOOLS
.
196
89
1
WHAT
TYPES
OF
ML
TOOLS
ARE
THERE?
.
196
90
1
WHAT
IS
PYTHON?
.
199
91
I
WHAT
IS
R
AND
RSTUDIO?
.
199
92
I
WHAT
IS
SCIKIT-LEARN?
.
200
93
1
WHAT
ARE
TENSORFLOW
AND
KERAS?
.
200
94
1
WHAT
ARE
MLLIB,
PYSPARK
AND
SPARKR?
.
201
95
I
WHAT
ARE
SOME
POPULAR
CLOUD-BASED
ML
TOOLS?
.
201
7
CREATING
AND
TESTING
A
ML
MODEL
WITH
SUPERVISED
MACHINE
LEARNING
.
203
7_1
CREATING
A
MACHINE
LEARNING
MODEL
WITH
SUPERVISED
ML
METHODS
.
203
XII
96
1
WHAT
INGREDIENTS
DO
YOU
NEED
AND
WHAT
IS
THE
RECIPE
FOR
CREATING
AN
ML
MODEL?
.
203
97
1
WHAT
IS
AN
ML
MODEL?
.
205
98
1
WHAT
IS
A
CORRELATION
AND
WHY
IS
IT
NECESSARY
FOR
ML
MODELS?
.
208
99
1
WHAT
IS
FEATURE
ENGINEERING
AND
WHY
IS
IT
CONSIDERED
*
APPLIED
ML
*
?
.
213
100
1
WHAT
IS
FEATURE
SELECTION
AND
WHY
IS
IT
NECESSARY?
.
215
101
I
WHY
DO
WE
NEED
TO
SPLIT
A
DATASET
INTO
TRAINING,
VALIDATION
AND
TEST
SETS?
.
219
102
1
WHAT
DOES
IT
MEAN
TO
*
TRAIN
AN
ML
MODEL
*
AND
HOW
DO
YOU
DO
IT?
.
222
7_2
VALIDATING,
TESTING
AND
USING
A
MACHINE
LEARNING
MODEL
.
227
103
1
WHAT
DOES
IT
MEAN
TO
*
VALIDATE
A
MODEL
*
,
AND
WHY
IS
IT
NECESSARY?
.
227
104
1
WHAT
IS
THE
DIFFERENCE
BETWEEN
VALIDATING
AND
TESTING
A
MODEL
AND
WHY
IS
THE
LATTER
NECESSARY?
.
231
105
1
WHAT
ARE
OVERFITTING
AND
GENERALIZATION?
.
233
106
1
PREVENTING
OVERFITTING:
HOW
DOES
CROSS-VALIDATION
WORK?
.
237
107
1
PREVENTING
OVERFITTING:
HOW
DOES
ENSEMBLE
LEARNING
WORK?
.
238
108
1
HOW
ELSE
CAN
OVERFITTING
BE
PREVENTED?
.
240
109
1
HOW
MUCH
DATA
IS
NEEDED
TO
TRAIN
AN
ML
MODEL?
.
240
8
POPULAR
MACHINE
LEARNING
MODEL
CLASSES
FOR
SUPERVISED
MACHINE
LEARNING
.
243
8_1
SOME
CLASSIC
ML
MODELS
.
243
1101
WHAT
MODEL
CLASSES
ARE
THERE
IN
ML?
.
243
1111
HOW
DO
GENERALIZED
LINEAR
MODELS
WORK?
.
244
1121
HOW
DO
DECISION
TREES
WORK?
.
245
1131
HOW
DO
ENSEMBLE
METHODS
SUCH
AS
THE
RANDOM
FOREST
ALGORITHM
WORK?
.
246
XIII
1141
HOW
DO
WE
CHOOSE
THE
RIGHT
ML
MODEL?
.
247
8_2
NEURAL
NETWORKS
AND
DEEP
LEARNING
.
249
1151
WHAT
ARE
NEURAL
NETWORKS
AND
DEEP
LEARNING
AND
WHY
DO
THEY
MATTER?
.
249
1161
HOW
DO
NEURAL
NETWORKS
WORK?
.
253
1171
WHAT
IS
SO
SPECIAL
ABOUT
DEEP
NEURAL
NETWORKS
COMPARED
TO
CLASSIC
ML
MODEL
CLASSES?
.
256
1181
WHY
ARE
NEURAL
NETWORKS
SO
GOOD
AT
NATURAL
LANGUAGE
PROCESSING
AND
COMPUTER
VISION?
.
259
119
1
ARE
NEURAL
NETWORKS
A
UNIVERSAL
CURE
FOR
ALL
ML
PROBLEMS
OR
DO
THEY
ALSO
HAVE
SOME
DRAWBACKS?
.
263
120
1
WHAT
IS
TRANSFER
LEARNING?
.
266
121
I
DEEP
NEURAL
NETWORKS
-
WHY
NOW
AND
WHAT
WILL
THEIR
FUTURE
LOOK
LIKE?
.
268
9
MANAGING
MACHINE
LEARNING
IN
A
COMPANY
.
271
9_1
PHASES
OF
AN
ML
PROJECT
.
271
122
I
HOW
DOES
THE
ML
PROCESS
WORK
(AN
OVERVIEW)?
.
271
123
1
PHASE
1:
HOW
CAN
ML
USE
CASES
BE
IDENTIFIED?
.
273
124
1
PHASE
2:
WHAT
ARE
DATA
EXPLORATION
AND
DATA
PREPARATION
AND
WHY
ARE
THEY
NECESSARY?
.
276
125
I
PHASE
3:
WHAT
IS
MODEL
CREATION?
.
282
126
1
PHASE
4:
WHAT
IS
(CONTINUOUS)
MODEL
DEPLOYMENT?
.
282
9_2
LESSONS
LEARNED
FROM
MACHINE-LEARNING
PROJECTS
.
285
127
1
HOW
LONG
DOES
A
MACHINE-LEARNING
PROJECT
TAKE
FROM
THE
CONCEPTION
OF
THE
IDEA
UNTIL
THE
MODEL
IS
DEPLOYED?
.
285
128
1
HOW
MANY
PROJECTS
MAKE
IT
FROM
THE
IDEA
TO
THE
END
AND
WHERE
DO
THEY
FAIL?
.
286
129
1
WHAT
ARE
THE
MOST
COMMON
REASONS
WHY
PROJECTS
FAIL?
.
287
130
1
WHY
IS
MODEL
DEPLOYMENT
THE
BOTTLENECK
FOR
MOST
COMPANIES
IMPLEMENTING
ML
PROJECTS?
.
289
XIV
9_3
PEOPLE
AND
JOB
ROLES
IN
ML
.
291
131
I
WHICH
ROLES
ARE
REQUIRED
TO
IMPLEMENT
AN
ML
PROJECT?
.
291
132
1
WHAT
DOES
A
DATA
SCIENTIST
DO?
.
293
133
1
WHAT
DOES
A
DATA
ENGINEER
DO?
.
293
134
1
WHAT
DOES
AN
ML
ENGINEER
DO?
.
294
135
1
WHAT
DOES
A
STATISTICIAN
DO?
.
294
136
1
WHAT
DOES
A
SOFTWARE
ENGINEER
DO?
.
295
137
1
WHAT
DOES
A
BUSINESS
ANALYST
DO?
.
295
138
1
WHAT
DO
OTHER
ROLES
DO?
.
296
9_4
AGILE
ORGANIZATION
AND
WAYS
OF
WORKING
.
296
139
1
WHAT
IS
AGILE
PROJECT
MANAGEMENT
AND
WHY
IS
IT
APPROPRIATE
FOR
ML
PROJECTS?
.
296
140
1
WHAT
ARE
DEVOPS
AND
DATAOPS?
.
300
141
I
WHAT
ARE
THE
POPULAR
ORGANIZATIONAL
STRUCTURES
AND
BEST
PRACTICES?
.
302
9_5
DATA
ETHICS
IN
ML
.
306
142
1
WHAT
IS
DATA
ETHICS?
.
306
143
I
WHAT
ARE
THE
ETHICAL
CONSIDERATIONS
IN
DATA
COLLECTION?
.
307
144
1
WHAT
ARE
THE
ETHICAL
CONSIDERATIONS
WHEN
CREATING
ML
MODELS?
.
309
145
1
WHAT
BEST
PRACTICES
AND
PRINCIPLES
CAN
ENSURE
THE
ETHICAL
USE
OF
DATA?
.
312
PART
IVI
WHERE
WILL
WE
GO?
10
THE
FUTURE
OF
DATA,
MACHINE
LEARNING
AND
ARTIFICIAL
INTELLIGENCE
.
316
146
1
HOW
ARE
AL
AND
ITS
DRIVERS
GOING
TO
DEVELOP?
.
316
147
1
WHAT
ARE
THE
IMPLICATIONS
OF
ML
AND
AL
FOR
COMPANIES?
.
319
148
1
WE
BENEFIT
A
LOT
FROM
AL,
BUT
WILL
IT
COST
ME
MY
JOB?
.
322
149
1
WHICH
NATION
WILL
WIN
THE
AL
RACE?
.
325
150
1
WHEN
ARE
WE
GOING
TO
SEE
THE
CREATION
OF
GENERAL
AL?
.
329
XV
APPENDIX
LIST
OF
ABBREVIATIONS
.
334
LIST
OF
TABLES
.
335
LIST
OF
FIGURES
.
339
LIST
OF
CASE
STUDIES
.
343
REFERENCE
LIST
.
346
INDEX
.
365
ABOUT
THE
AUTHORS
.
371
XVI |
| adam_txt |
TABLE
OF
CONTENTS
FOREWORD
.
XVII
PREFACE
.
XVIII
ACKNOWLEDGMENTS
.
XXI
INTRODUCTION
.
XXII
PART
11
WHY
DO
WE
CARE:
THE
DIGITAL
TRANSFORMATION
TRAIN
1
DIGITAL
TRANSFORMATION:
THE
ROLE
OF
DATA
AND
ARTIFICIAL
INTELLIGENCE.
2
1_1
DIGITAL
TRANSFORMATION
.
2
I
I
WHAT
IS
DIGITAL
TRANSFORMATION?
.
2
2
1
WHAT
IS
THE
IMPACT
OF
DIGITAL
TRANSFORMATION
ON
COMPANIES
AND
SOCIETY?
.
4
3
1
WHAT
ARE
THE
DRIVERS
OF
DIGITAL
TRANSFORMATION?
.
7
1
2
THE
ROLE
OF
DATA
AND
AL
IN
DIGITAL
TRANSFORMATION
.
8
4
1
AL
-
WHY
IS
IT
THE
ENGINE
OF
DIGITAL
TRANSFORMATION?
.
8
5
1
DATA
-
WHY
IS
IT
THE
FUEL
OF
DIGITAL
TRANSFORMATION?
.
9
6
1
HOW
ARE
DATA
AND
AL
APPLIED
TO
GENERATE
VALUE
ACROSS
INDUSTRIES?
.
10
1_3
BUZZWORDS
IN
DIGITAL
TRANSFORMATION,
DATA
AND
AL
.
13
7
1
WHAT
IS
AN
OVERVIEW
OF
BUZZWORDS
IN
DATA
AND
AL?
.
13
8
1
WHAT
IS
THE
LOT
AND
WHAT
DOES
IT
HAVE
TO
DO
WITH
BIG
DATA?
.
14
9
1
WHAT
ARE
DATA
LAKES,
DATA
WAREHOUSES,
DATA
ARCHITECTURES,
HADOOP
AND
NOSQL
DATABASES?
.
15
10
1
WHAT
ARE
DATA
GOVERNANCE
AND
DATA
DEMOCRATIZATION?
.
16
II
I
WHAT
IS
THE
CLOUD?
.
16
12
I
WHAT
ARE
DATA
SCIENCE,
DATA
ANALYTICS,
BUSINESS
INTELLIGENCE,
DATA
MINING
AND
PREDICTIVE
ANALYTICS?
.
17
VLI
13
1
WHAT
ARE
MACHINE
LEARNING,
NEURAL
NETWORKS
AND
DEEP
LEARNING?
.
19
14
1
WHAT
ARE
AL,
NATURAL
LANGUAGE
PROCESSING,
COMPUTER
VISION
AND
ROBOTICS?
.
20
PART
III
THE
FUEL:
DATA
2
UNDERSTANDING
DATA:
THE
FUEL
OF
DIGITAL
AND
ARTIFICIAL
INTELLIGENCE
TRANSFORMATION
.
22
2_1
UNDERSTANDING
DATA
.
22
15
1
WHAT
IS
DATA?
.
22
16
1
WHY
COLLECT
DATA
AND
WHAT
ARE
THE
DIFFERENT
TYPES
OF
DATA
ANALYTICS?
.
28
17
1
HOW
IS
DATA
CREATED?
.
30
18
I
WHAT
ARE
THE
FACTORS
THAT
HAVE
ENABLED
AN
ERA
OF
MASS
DATA
CREATION
AND
STORAGE?
.
32
19
1
WHAT
IS
DATA
QUALITY
AND
WHAT
KIND
OF
DATA
QUALITY
ISSUES
ARE
THERE?
.
36
20
1
HOW
MUCH
DATA
QUALITY
DO
YOU
NEED?
.
39
2_2
TYPES
OF
DATA
.
41
21
I
WHAT
ARE
UNSTRUCTURED,
SEMI-STRUCTURED
AND
STRUCTURED
DATA?
.
41
22
1
WHAT
ARE
MASTER
DATA
AND
TRANSACTIONAL
DATA?
.
45
23
1
WHAT
IS
STREAMING
DATA
AND
WHAT
IS
THE
DIFFERENCE
BETWEEN
BATCH
AND
STREAMING
PROCESSING?
.
46
24
1
WHAT
IS
BIG
DATA?
.
48
3
DATA
STORAGE
TECHNOLOGIES
.
52
3_1
UNDERSTANDING
DATA
STORAGE
.
52
25
I
WHY
CAN
*
T
A
COMPANY
STORE
ITS
STRUCTURED
DATA
IN
AN
EXCEL
FILE
LIKE
WE
DO
ON
PCS?
.
52
26
1
WHAT
IS
A
DATABASE
AND
HOW
DOES
IT
WORK?
.
54
27
1
WHAT
ARE
THE
ADVANTAGES
OF
STORING
DATA
IN
A
DATABASE?
.
56
VIII
28
1
WHAT
TYPES
OF
DATABASES
ARE
THERE
AND
HOW
ARE
THEY
CLASSIFIED?
.
57
3_2
RELATIONAL
(SQL)
DATABASES
.
59
29
1
WHAT
IS
A
RELATIONAL
DATABASE
SYSTEM
AND
HOW
DOES
IT
WORK?
.
59
30
1
HOW
DOES
THE
RELATIONAL
MODEL
WORK?
.
61
31
I
WHAT
IS
A
KEY
ATTRIBUTE
AND
WHY
IS
IT
INDISPENSABLE?
.
64
32
1
HOW
IS
DATA
ACCESSED
AND
MANIPULATED
IN
A
RELATIONAL
DATABASE
SYSTEM
(SQL)?
.
68
33
1
WHAT
ARE
THE
STRENGTHS
OF
RELATIONAL
DATABASE
SYSTEMS?
.
72
34
1
WHAT
ARE
THE
LIMITATIONS
OF
RELATIONAL
DATABASE
SYSTEMS
AND
HOW
WERE
THEY
REVEALED
WITH
THE
DAWN
OF
BIG
DATA?
.
73
3_3
DISTRIBUTED
FILE
SYSTEMS
AND
NON-RELATIONAL
(NOSQL)
DATABASES
.
76
35
1
WHAT
ARE
COMPUTER
CLUSTERS
AND
HOW
DID
THE
IDEA
OF
*
SCALING
OUT"
FORM
THE
BASIS
FOR
STORING
AND
PROCESSING
BIG
DATA?
.
76
36
1
WHAT
ARE
DISTRIBUTED
FILE
SYSTEMS
AND
HOW
DO
WE
STORE
DATA
WITH
THEM?
.
79
37
I
WHAT
ARE
NON-RELATIONAL
(NOSQL)
DATABASES
AND
WHAT
DOES
THE
CAP
THEOREM
HAVE
TO
DO
WITH
THEM?
.
81
38
1
HOW
DO
RELATIONAL
AND
NON-RELATIONAL
DATABASES
COMPARE
AND
WHEN
IS
IT
BEST
TO
USE
EACH
ONE?
.
88
3_4
POPULAR
DATA
STORAGE
TECHNOLOGIES
.
90
39
1
WHAT
ARE
THE
TYPES
OF
DATA
STORAGE
TECHNOLOGIES?
.
90
40
1
WHAT
ARE
HADOOP
AND
THE
HADOOP
ECOSYSTEM
(E.G.
HIVE,
HBASE,
FLUME,
KAFKA)?
.
91
41
I
WHAT
IS
SPARK?
.
93
42
1
WHAT
ARE
MYSQL,
POSTGRESQL,
ORACLE,
MICROSOFT
SQL
SERVER,
SAP
HANA,
IBM
DB2
AND
TERADATA
DATABASE?
.
94
IX
43
1
WHAT
ARE
MONGODB,
NEO4J,
AMAZON
DYNAMODB,
COUCHDB
AND
REDIS?
.
95
4
ARCHITECTING
DATA:
DATA
WAREHOUSES,
DATA
LAKES
AND
THE
CLOUD
.
96
4_1
UNDERSTANDING
DATA
ARCHITECTURES
.
96
44
1
WHAT
IS
A
DATA
ARCHITECTURE
AND
WHY
DO
COMPANIES
NEED
IT?
.
96
45
1
WHAT
ARE
THE
MOST
POPULAR
ARCHITECTURAL
BLUEPRINTS?
.
99
4_2
DATA
WAREHOUSE
ARCHITECTURES
.
100
46
1
WHAT
IS
A
DATA
WAREHOUSE
(DWH)
ARCHITECTURE?
.
100
47
1
HOW
DOES
A
DWH
WORK?
.
103
48
1
WHAT
DOES
A
TYPICAL
DATA
PIPELINE
IN
A
DWH
LOOK
LIKE?
.
107
49
I
WHAT
ARE
THE
LIMITATIONS
OF
A
DWH?
.
108
50
1
WHAT
ARE
POPULAR
ETL
TOOLS?
.
110
4_3
DATA
LAKES
AND
STREAMING
ARCHITECTURES
.
111
51
I
WHAT
IS
A
DATA
LAKE
ARCHITECTURE?
.
111
52
1
HOW
DOES
A
DATA
LAKE
WORK
AND
WHERE
SHOULD
IT
BE
USED?
.
112
53
1
HOW
DO
A
DWH
AND
DATA
LAKE
COMPARE?
.
115
4_4
CLOUD
ARCHITECTURES
.
118
54
1
WHAT
IS
THE
CLOUD?
.
118
55
1
WHAT
TYPES
OF
CLOUD
ARCHITECTURES
ARE
THERE?
.
120
56
1
WHAT
TYPES
OF
CLOUD
SERVICES
ARE
THERE?
.
123
57
1
WHAT
ARE
THE
ADVANTAGES
AND
DISADVANTAGES
OF
USING
CLOUD
SERVICES?
.
126
58
1
WHAT
IS
A
SERVERLESS
ARCHITECTURE?
.
130
59
I
WHAT
ARE
THE
POPULAR
CLOUD
PROVIDERS
AND
SERVICES?
.
132
5
MANAGING
DATA
IN
A
COMPANY
.
134
5_1
PEOPLE
AND
JOB
ROLES
.
134
60
1
WHAT
DOES
A
CHIEF
DATA
AND
ANALYTICS
OFFICER
DO?
.
134
61
I
WHAT
DOES
A
DATA
ARCHITECT
DO?
.
135
62
1
WHAT
DOES
A
DATABASE
ADMINISTRATOR
DO?
.
136
X
63
1
WHAT
OTHER
JOB
ROLES
ARE
INVOLVED
IN
CREATING
AND
MAINTAINING
A
DATA
ARCHITECTURE?
.
136
5_2
DATA
GOVERNANCE
AND
DEMOCRATIZATION
.
137
64
1
WHAT
ARE
DATA
GOVERNANCE
AND
DEMOCRATIZATION
AND
WHY
DOES
DATA
NEED
TO
BE
GOVERNED
AND
DEMOCRATIZED?
.
137
65
1
WHAT
ARE
THE
KEY
ELEMENTS
OF
DATA
GOVERNANCE
AND
DATA
DEMOCRATIZATION?
.
138
66
1
HOW
CAN
WE
MAKE
DATA
MORE
FINDABLE
AND
ACCESSIBLE?
.
139
67
1
HOW
CAN
WE
MAKE
DATA
MORE
UNDERSTANDABLE
AND
SHARE
KNOWLEDGE
ON
DATA?
.
140
68
1
HOW
CAN
WE
MAKE
DATA
MORE
TRUSTWORTHY
AND
IMPROVE
THE
QUALITY
OF
DATA?
.
141
69
1
HOW
CAN
WE
EMPOWER
THE
DATA
USER
WITH
SELF-SERVICE
BL
AND
ANALYTICS?
.
141
70
1
HOW
CAN
DATA
GOVERNANCE
AND
DATA
DEMOCRATIZATION
BE
IMPLEMENTED?
.
142
5_3
DATA
SECURITY
AND
PROTECTION
(PRIVACY)
.
143
71
I
WHAT
IS
AN
OVERVIEW
OF
DATA
SECURITY,
DATA
PROTECTION
AND
DATA
PRIVACY
AND
HOW
DO
THEY
RELATE
TO
EACH
OTHER?
.
143
72
1
WHAT
IS
DATA
SECURITY
AND
HOW
CAN
IT
BE
ACHIEVED?
.
145
73
1
WHAT
IS
PERSONAL
DATA?
.
148
74
1
WHAT
IS
DATA
PROTECTION
(PRIVACY)
AND
WHY
IS
THE
DISTINCTION
BETWEEN
NON-PERSONAL
AND
PERSONAL
DATA
SO
IMPORTANT?
.
150
75
1
GENERAL
DATA
PROTECTION
REGULATION
(GDPR)
-
WHO,
WHAT,
WHERE
AND
WHY?
.
153
PART
III
I
THE
ENGINE:
ARTIFICIAL
INTELLIGENCE
AND
MACHINE
LEARNING
6
UNDERSTANDING
MACHINE
LEARNING
AS
THE
KEY
DRIVER
BEHIND
ARTIFICIAL
INTELLIGENCE
.
160
6_1
UNDERSTANDING
AL
AND
ML
.
160
76
1
WHAT
IS
AL?
.
160
XI
77
1
WHERE
CAN
AL
BE
APPLIED
AND
HOW
HAVE
APPROACHES
TO
CREATE
AL
DEVELOPED
OVER
TIME?
.
163
78
1
WHAT
IS
CURRENTLY
POSSIBLE
WITH
AL
AND
WHAT
ARE
SOME
TOP
BREAKTHROUGHS?
.
165
79
1
WHY
IS
AL
ALMOST
TANTAMOUNT
TO
ML
(AL
=
ML
+
X)
TODAY?
.
168
80
1
WHAT
IS
ML
AND
HOW
CAN
IT
CREATE
AL?
.
170
81
I
HOW
IS
A
MACHINE
ABLE
TO
LEARN
AND
WHY
IS
ML
OFTEN
CONSIDERED
*
SOFTWARE
2.0
*
?
.
172
82
1
WHAT
IS
A
MACHINE
ABLE
TO
LEAM
-
CAN
IT
PREDICT
THE
FUTURE?
.
176
6_2
TYPES
OF
ML
.
180
83
1
WHAT
TYPES
OF
ML
ARE
THERE
AND
HOW
DO
THEY
DIFFER?
.
180
84
1
WHAT
IS
SUPERVISED
ML?
.
184
85
1
WHAT
IS
THE
DIFFERENCE
BETWEEN
REGRESSION
AND
CLASSIFICATION?
.
186
86
1
WHAT
IS
UNSUPERVISED
ML?
.
187
87
1
WHAT
ARE
THE
MOST COMMONLY
USED
METHODS
IN
UNSUPERVISED
LEARNING?
.
189
88
1
WHAT
IS
REINFORCEMENT
LEARNING?
.
193
6_3
POPULAR
ML
TOOLS
.
196
89
1
WHAT
TYPES
OF
ML
TOOLS
ARE
THERE?
.
196
90
1
WHAT
IS
PYTHON?
.
199
91
I
WHAT
IS
R
AND
RSTUDIO?
.
199
92
I
WHAT
IS
SCIKIT-LEARN?
.
200
93
1
WHAT
ARE
TENSORFLOW
AND
KERAS?
.
200
94
1
WHAT
ARE
MLLIB,
PYSPARK
AND
SPARKR?
.
201
95
I
WHAT
ARE
SOME
POPULAR
CLOUD-BASED
ML
TOOLS?
.
201
7
CREATING
AND
TESTING
A
ML
MODEL
WITH
SUPERVISED
MACHINE
LEARNING
.
203
7_1
CREATING
A
MACHINE
LEARNING
MODEL
WITH
SUPERVISED
ML
METHODS
.
203
XII
96
1
WHAT
INGREDIENTS
DO
YOU
NEED
AND
WHAT
IS
THE
RECIPE
FOR
CREATING
AN
ML
MODEL?
.
203
97
1
WHAT
IS
AN
ML
MODEL?
.
205
98
1
WHAT
IS
A
CORRELATION
AND
WHY
IS
IT
NECESSARY
FOR
ML
MODELS?
.
208
99
1
WHAT
IS
FEATURE
ENGINEERING
AND
WHY
IS
IT
CONSIDERED
*
APPLIED
ML
*
?
.
213
100
1
WHAT
IS
FEATURE
SELECTION
AND
WHY
IS
IT
NECESSARY?
.
215
101
I
WHY
DO
WE
NEED
TO
SPLIT
A
DATASET
INTO
TRAINING,
VALIDATION
AND
TEST
SETS?
.
219
102
1
WHAT
DOES
IT
MEAN
TO
*
TRAIN
AN
ML
MODEL
*
AND
HOW
DO
YOU
DO
IT?
.
222
7_2
VALIDATING,
TESTING
AND
USING
A
MACHINE
LEARNING
MODEL
.
227
103
1
WHAT
DOES
IT
MEAN
TO
*
VALIDATE
A
MODEL
*
,
AND
WHY
IS
IT
NECESSARY?
.
227
104
1
WHAT
IS
THE
DIFFERENCE
BETWEEN
VALIDATING
AND
TESTING
A
MODEL
AND
WHY
IS
THE
LATTER
NECESSARY?
.
231
105
1
WHAT
ARE
OVERFITTING
AND
GENERALIZATION?
.
233
106
1
PREVENTING
OVERFITTING:
HOW
DOES
CROSS-VALIDATION
WORK?
.
237
107
1
PREVENTING
OVERFITTING:
HOW
DOES
ENSEMBLE
LEARNING
WORK?
.
238
108
1
HOW
ELSE
CAN
OVERFITTING
BE
PREVENTED?
.
240
109
1
HOW
MUCH
DATA
IS
NEEDED
TO
TRAIN
AN
ML
MODEL?
.
240
8
POPULAR
MACHINE
LEARNING
MODEL
CLASSES
FOR
SUPERVISED
MACHINE
LEARNING
.
243
8_1
SOME
CLASSIC
ML
MODELS
.
243
1101
WHAT
MODEL
CLASSES
ARE
THERE
IN
ML?
.
243
1111
HOW
DO
GENERALIZED
LINEAR
MODELS
WORK?
.
244
1121
HOW
DO
DECISION
TREES
WORK?
.
245
1131
HOW
DO
ENSEMBLE
METHODS
SUCH
AS
THE
RANDOM
FOREST
ALGORITHM
WORK?
.
246
XIII
1141
HOW
DO
WE
CHOOSE
THE
RIGHT
ML
MODEL?
.
247
8_2
NEURAL
NETWORKS
AND
DEEP
LEARNING
.
249
1151
WHAT
ARE
NEURAL
NETWORKS
AND
DEEP
LEARNING
AND
WHY
DO
THEY
MATTER?
.
249
1161
HOW
DO
NEURAL
NETWORKS
WORK?
.
253
1171
WHAT
IS
SO
SPECIAL
ABOUT
DEEP
NEURAL
NETWORKS
COMPARED
TO
CLASSIC
ML
MODEL
CLASSES?
.
256
1181
WHY
ARE
NEURAL
NETWORKS
SO
GOOD
AT
NATURAL
LANGUAGE
PROCESSING
AND
COMPUTER
VISION?
.
259
119
1
ARE
NEURAL
NETWORKS
A
UNIVERSAL
CURE
FOR
ALL
ML
PROBLEMS
OR
DO
THEY
ALSO
HAVE
SOME
DRAWBACKS?
.
263
120
1
WHAT
IS
TRANSFER
LEARNING?
.
266
121
I
DEEP
NEURAL
NETWORKS
-
WHY
NOW
AND
WHAT
WILL
THEIR
FUTURE
LOOK
LIKE?
.
268
9
MANAGING
MACHINE
LEARNING
IN
A
COMPANY
.
271
9_1
PHASES
OF
AN
ML
PROJECT
.
271
122
I
HOW
DOES
THE
ML
PROCESS
WORK
(AN
OVERVIEW)?
.
271
123
1
PHASE
1:
HOW
CAN
ML
USE
CASES
BE
IDENTIFIED?
.
273
124
1
PHASE
2:
WHAT
ARE
DATA
EXPLORATION
AND
DATA
PREPARATION
AND
WHY
ARE
THEY
NECESSARY?
.
276
125
I
PHASE
3:
WHAT
IS
MODEL
CREATION?
.
282
126
1
PHASE
4:
WHAT
IS
(CONTINUOUS)
MODEL
DEPLOYMENT?
.
282
9_2
LESSONS
LEARNED
FROM
MACHINE-LEARNING
PROJECTS
.
285
127
1
HOW
LONG
DOES
A
MACHINE-LEARNING
PROJECT
TAKE
FROM
THE
CONCEPTION
OF
THE
IDEA
UNTIL
THE
MODEL
IS
DEPLOYED?
.
285
128
1
HOW
MANY
PROJECTS
MAKE
IT
FROM
THE
IDEA
TO
THE
END
AND
WHERE
DO
THEY
FAIL?
.
286
129
1
WHAT
ARE
THE
MOST
COMMON
REASONS
WHY
PROJECTS
FAIL?
.
287
130
1
WHY
IS
MODEL
DEPLOYMENT
THE
BOTTLENECK
FOR
MOST
COMPANIES
IMPLEMENTING
ML
PROJECTS?
.
289
XIV
9_3
PEOPLE
AND
JOB
ROLES
IN
ML
.
291
131
I
WHICH
ROLES
ARE
REQUIRED
TO
IMPLEMENT
AN
ML
PROJECT?
.
291
132
1
WHAT
DOES
A
DATA
SCIENTIST
DO?
.
293
133
1
WHAT
DOES
A
DATA
ENGINEER
DO?
.
293
134
1
WHAT
DOES
AN
ML
ENGINEER
DO?
.
294
135
1
WHAT
DOES
A
STATISTICIAN
DO?
.
294
136
1
WHAT
DOES
A
SOFTWARE
ENGINEER
DO?
.
295
137
1
WHAT
DOES
A
BUSINESS
ANALYST
DO?
.
295
138
1
WHAT
DO
OTHER
ROLES
DO?
.
296
9_4
AGILE
ORGANIZATION
AND
WAYS
OF
WORKING
.
296
139
1
WHAT
IS
AGILE
PROJECT
MANAGEMENT
AND
WHY
IS
IT
APPROPRIATE
FOR
ML
PROJECTS?
.
296
140
1
WHAT
ARE
DEVOPS
AND
DATAOPS?
.
300
141
I
WHAT
ARE
THE
POPULAR
ORGANIZATIONAL
STRUCTURES
AND
BEST
PRACTICES?
.
302
9_5
DATA
ETHICS
IN
ML
.
306
142
1
WHAT
IS
DATA
ETHICS?
.
306
143
I
WHAT
ARE
THE
ETHICAL
CONSIDERATIONS
IN
DATA
COLLECTION?
.
307
144
1
WHAT
ARE
THE
ETHICAL
CONSIDERATIONS
WHEN
CREATING
ML
MODELS?
.
309
145
1
WHAT
BEST
PRACTICES
AND
PRINCIPLES
CAN
ENSURE
THE
ETHICAL
USE
OF
DATA?
.
312
PART
IVI
WHERE
WILL
WE
GO?
10
THE
FUTURE
OF
DATA,
MACHINE
LEARNING
AND
ARTIFICIAL
INTELLIGENCE
.
316
146
1
HOW
ARE
AL
AND
ITS
DRIVERS
GOING
TO
DEVELOP?
.
316
147
1
WHAT
ARE
THE
IMPLICATIONS
OF
ML
AND
AL
FOR
COMPANIES?
.
319
148
1
WE
BENEFIT
A
LOT
FROM
AL,
BUT
WILL
IT
COST
ME
MY
JOB?
.
322
149
1
WHICH
NATION
WILL
WIN
THE
AL
RACE?
.
325
150
1
WHEN
ARE
WE
GOING
TO
SEE
THE
CREATION
OF
GENERAL
AL?
.
329
XV
APPENDIX
LIST
OF
ABBREVIATIONS
.
334
LIST
OF
TABLES
.
335
LIST
OF
FIGURES
.
339
LIST
OF
CASE
STUDIES
.
343
REFERENCE
LIST
.
346
INDEX
.
365
ABOUT
THE
AUTHORS
.
371
XVI |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Thamm, Alexander 1982- Gramlich, Michael Borek, Alexander |
| author_GND | (DE-588)1225376793 (DE-588)1023903156 |
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| ctrlnum | (OCoLC)1151743844 (DE-599)DNB1207861898 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
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| id | DE-604.BV046942651 |
| illustrated | Illustrated |
| index_date | 2024-07-03T15:38:37Z |
| indexdate | 2024-07-20T04:06:34Z |
| institution | BVB |
| isbn | 9783982173702 3982173701 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032351329 |
| oclc_num | 1151743844 |
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| physical | xxviii, 371 Seiten Illustrationen, Diagramme 23.4 cm x 15.6 cm |
| publishDate | 2020 |
| publishDateSearch | 2020 |
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| publisher | Data AI Press |
| record_format | marc |
| spelling | Thamm, Alexander 1982- Verfasser (DE-588)1225376793 aut The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data Alexander Thamm, Michael Gramlich, Dr. Alexander Borek München Data AI Press [2020] © 2020 xxviii, 371 Seiten Illustrationen, Diagramme 23.4 cm x 15.6 cm txt rdacontent n rdamedia nc rdacarrier Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen Machine Learning Artificial Intelligence Künstliche Intelligenz DSGVO Data Science Datenbank Big Data Cloud Daten Analytics Maschinelles Lernen (DE-588)4193754-5 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Gramlich, Michael Verfasser aut Borek, Alexander Verfasser (DE-588)1023903156 aut Erscheint auch als Online-Ausgabe 978-3-9821737-1-9 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032351329&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Thamm, Alexander 1982- Gramlich, Michael Borek, Alexander The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4193754-5 |
| title | The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data |
| title_auth | The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data |
| title_exact_search | The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data |
| title_exact_search_txtP | The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data |
| title_full | The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data Alexander Thamm, Michael Gramlich, Dr. Alexander Borek |
| title_fullStr | The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data Alexander Thamm, Michael Gramlich, Dr. Alexander Borek |
| title_full_unstemmed | The ultimate data and AI guide 150 FAQs about artificial intelligence, machine learning and data Alexander Thamm, Michael Gramlich, Dr. Alexander Borek |
| title_short | The ultimate data and AI guide |
| title_sort | the ultimate data and ai guide 150 faqs about artificial intelligence machine learning and data |
| title_sub | 150 FAQs about artificial intelligence, machine learning and data |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Künstliche Intelligenz Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032351329&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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