Artificial intelligence for humans: 1 Fundamental algorithms
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Charleston
Createspace
2013
|
| Online-Zugang: | Klappentext Inhaltsverzeichnis |
| Beschreibung: | xxviii, 194 Seiten Illustrationen |
| ISBN: | 9781493682225 |
Internformat
MARC
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| 245 | 1 | 0 | |a Artificial intelligence for humans |n 1 |p Fundamental algorithms |
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| 300 | |a xxviii, 194 Seiten |b Illustrationen | ||
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Datensatz im Suchindex
| _version_ | 1804175823245148160 |
|---|---|
| adam_text | A great building requires a strong foundation. This book teaches ba-
sic Artificial Intelligence algorithms such as dimensionality, distance
metrics, clustering, error calculation, hill climbing, Nelder Mead, and
linear regression. These are not just foundational algorithms for the
rest of the series, but are very useful in their own right. The book
explains all algorithms using actual numeric calculations that you can
perform yourself.
Artificial Intelligence for Humans is a book series meant to teach Al
to those without an extensive mathematical background. The read-
er needs only a knowledge of basic college algebra or computer
programming—anything more complicated than that is thoroughly
explained. Every chapter also includes a programming example. Exam-
ples are currently provided in Java, C#, R, Python and C. Other lan-
guages are planned.
xi
Contents
Introduction xix
0.1 Series Introduction................................. xix
0.1.1 Programming Languages............................... xx
0.1.2 Online Labs...................................... xxi
0.1.3 Code Repositories . ............................... xxi
0.1.4 Books Planned for the Series...................... xxii
0.1.5 Other Resources..................................... xxiii
0.2 Fundamental Algorithms Introduction ...................... xxiv
0.3 Structure of this Book.................................... xxiv
0.4 The Kickstarter Campaign................................. xxvii
1 Introduction to AI 1
1.1 Relationship to Human Brains............................... 2
1.1.1 The Brain and Its World ............................. 4
1.1.2 Brain in a Vat....................................... 5
1.2 Modeling Problems......................................... 7
1.2.1 Data Classification................................. 7
1.2.2 Regression Analysis................................ 9
1.2.3 Clustering ...................................... . 10
1.2.4 Time Series......................................... 11
1.3 Modeling Input and Output................................. 12
CONTENTS
xu
1.3.1 A Simple Example..................................... 15
1.3.2 Miles per Gallon .................................... 16
1.3.3 Presenting Images to Algorithms ..................... 18
1.3.4 Financial Algorithms................................. 20
1.4 Understanding Training .................................... 22
1.4.1 Evaluating Success................................... 22
1.4.2 Batch and Online Training............................ 23
1.4.3 Supervised and Unsupervised Training................. 23
1.4.4 Stochastic and Deterministic Training................ 23
1.5 Chapter Summary ........................................... 24
2 Normalization 27
2.1 Levels of Measurement...................................... 27
2.1.1 Quantitative Observations............................ 30
2.2 Normalizing Observations .................................. 31
2.2.1 Normalizing Nominal Observations..................... 32
2.2.2 Normalizing Ordinal Observations..................... 33
2.2.3 Denormalizing Ordinal Observations................... 35
2.2.4 Normalizing Quantitative Observations................ 37
2.2.5 Denormalizing Quantitative Observations.............. 38
2.3 Other Methods of Normalization ............................ 39
2.3.1 Reciprocal Normalization............................. 39
2.3.2 Reciprocal Denonnalization........................... 40
2.3.3 Understanding Equilateral Encoding................... 40
2.3.4 Implementing Equilateral Encoding.................... 43
2.4 Chapter Summary ........................................... 47
3 Distance Metrics 51
3.1 Understanding Vectors...................................... 51
CONTENTS
♦ ♦ ·
Xlll
3.2 Calculating Vector Distance............................... 53
3.2.1 Euclidean Distance................................. 53
3.2.2 Manhattan Distance................................. 55
3.2.3 Cliebyshev Distance ............................... 57
3.3 Optical Character Recognition............................. 59
3.4 Chapter Summary .......................................... 62
4 Random Number Generation 65
4.1 PRNG Concepts ........................................... 66
4.2 Random Distribution Types................................ 67
4.3 Roulette Wheels....................................... 70
4.4 PRNG Algorithms .......................................... 71
4.4.1 Linear Congruential Generator ..................... 71
4.4.2 Multiply with Carry................................ 73
4.4.3 Mersenne Twister................................... 74
4.4.4 Box Muller Transformation ........................ 76
4.5 Estimating PI with Monte Carlo............................ 77
4.6 Chapter Summary .......................................... 79
5 K-Means Clustering 83
5.1 Understanding Training Sets............................... 85
5.1.1 Unsupervised Training.............................. 85
5.1.2 Supervised Training ............................... 88
5.2 Understanding the K-Means Algorithm ............. 88
5.2.1 Assignment Step ................................. 89
5.2.2 Update Step........................................ 90
5.3 Initializing the K-Means Algorithm ....................... 91
5.3.1 Random K-Means Initialization...................... 92
5.3.2 Forgy K-Means Initialization ............... 95
XIV
CONTENTS
5.4 Chapter Summary ........................................... 97
6 Error Calculation 99
6.1 Sum of Squares Error...................................... 100
6.2 Root Mean Square.......................................... 101
6.3 Mean Square Error......................................... 101
6.4 Comparison of Error Calculation Methods................. · 102
6.4.1 Partitioning Training Data......................... 102
6.5 Chapter Summary ......................................... 104
7 Towards Machine Learning 107
7.1 Coefficients of a Polynomial.............................. 109
7.2 Introduction to Training.................................. Ill
7.2.1 Greedy Random Training ............................ Ill
7.3 Radial Basis Networks..................................... 114
7.3.1 Radial Basis Functions............................. 114
7.3.2 Radial Basis Function Networks..................... 118
7.3.3 Implementing an RBF Network........................ 120
7.3.4 Using an RBF Network............................... 125
7.4 Chapter Summary .......................................... 126
8 Optimization Training 129
8.0.1 Hill Climbing Training............................. 129
8.1 Simulated Annealing..................................... 133
8.1.1 Simulated Annealing Applications................... 134
8.1.2 Simulated Annealing Algorithm ............. 134
8.1.3 Cooling Schedule................................ 138
8.1.4 Annealing Probability ............................. 139
8.2 Nelder Mead ............................................. 140
8.2.1 Reflection ........................................ 143
CONTENTS
xv
8.2.2 Expansion......................................... 144
8.2.3 Contraction....................................... 145
8.3 Finishing the Nelder Mead Algorithm...................... 147
8.4 Chapter Summary ......................................... 148
9 Discrete Optimization 151
9.1 The Traveling Salesman Problem........................... 152
9.1.1 Understanding the Traveling Salesman Problem .... 152
9.1.2 Implementing the Traveling Salesman Problem...... 153
9.2 Circular TSP........................................... 155
9.3 The Knapsack Problem..................................... 156
9.3.1 Understanding the Knapsack Problem .............. 156
9.3.2 Implementing the Knapsack Problem................. 158
9.4 Chapter Summary ......................................... 160
10 Linear Regression 163
10.1 Linear Regression........................................ 164
10.1.1 Least Squares Fitting............................. 165
10.1.2 Least Squares Fitting Example .................... 167
10.1.3 Anscombe’s Quartet................................ 169
10.1.4 Abalone Data Set.................................. 170
10.2 Generalized Linear Models................................ 170
10.2.1 Reweight Least Squares Training .................. 173
10.3 Chapter Summary ......................................... 176
A Examples 179
A.l Artificial Intelligence for Humans....................... 179
A.2 Staying Up to Date...................................... 180
A.3 Obtaining the Examples.................................. 180
A.3.1 Download ZIP File.................................. 181
XVI
CONTENTS
A.3.2 Clone the Git Repository............................ 181
A.4 Example Contents .......................................... 182
A.5 Contributing to the Project ............................... 186
References
187
|
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| indexdate | 2024-07-10T07:22:24Z |
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| isbn | 9781493682225 |
| language | English |
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| spelling | Heaton, Jeff Verfasser (DE-588)1114194093 aut Artificial intelligence for humans 1 Fundamental algorithms Charleston Createspace 2013 xxviii, 194 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier (DE-604)BV043292383 1 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713496&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713496&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Heaton, Jeff Artificial intelligence for humans |
| title | Artificial intelligence for humans |
| title_auth | Artificial intelligence for humans |
| title_exact_search | Artificial intelligence for humans |
| title_full | Artificial intelligence for humans 1 Fundamental algorithms |
| title_fullStr | Artificial intelligence for humans 1 Fundamental algorithms |
| title_full_unstemmed | Artificial intelligence for humans 1 Fundamental algorithms |
| title_short | Artificial intelligence for humans |
| title_sort | artificial intelligence for humans fundamental algorithms |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713496&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713496&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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