Practical AI for business leaders, product managers, and entrepreneurs:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston ; Berlin
De Gruyter
[2022]
|
| Series: | Business and economics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVII, 221 Seiten Illustrationen, Diagramme |
| ISBN: | 9781501514647 |
Staff View
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Record in the Search Index
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| adam_text | Contents Acknowledgments---- VII Preface — XV 1 1.1 լ.շ 1.3 1.4 Introduction---- 1 Artificial Intelligence---- 1 What Is Machine Learning?------ 3 Areas of Machine Learning----- 4 Machine Learning Workflow------5 Part I: Machine Learning I 2 2.1 2.2 2.3 2.4 2.5 2.6 Simple Linear Regression - Concept — 9 Bird’s Eye View---- 9 Fundamental Equation of SLR---- 14 Some Assumptions of Simple Linear Regression---- 15 Case Study: Is Earning Related to Learning?---- 17 Interpreting Regression---- 20 Summary---- 22 3 3.1 3.2 3.3 3.4 3.5 3.6 Simple Linear Regression - Theory — 23 Some Basics of Measuring Distance---- 23 Total Absolute Error (TAE)---- 26 Residual Sum of Squares (RSS)--- 27 Mean Squared Error---- 28 Analytical vs. Numerical Solutions in Machine Learning Summary — 30 4 Simple Linear Regression - Practice — 31 5 5.1 5.2 5.3 5.4 К-Nearest Neighbors (KNN) - Concept---- 34 Bird’s Eye View---- 34 Prediction Structure---- 35 Case Study: To Loan or Not to Loan---- 38 Summary---- 42 6 6.1 6.2 К-Nearest Neighbors (KNN) - Theory---- 43 Distance Metrics---- 43 Choosing к in KNN---- 50
Λ ------ 6.3 6.4 7 Contents ML Pipelines, Hyperparameters-----52 Summary-----52 К-Nearest Neighbors (KNN) - Practice — 53 Partii: Model Assessment 8 8.1 8.2 8.3 8.4 8.5 9 Model Assessment - Bias-Variance Tradeoff — 59 Train-Test Split-----59 /Г-Fold Cross-Validation-----60 Underfit and Overfit-----60 Bias-Variance Tradeoff — 64 Summary----- 65 Model Assessment-Regression — 66 9.1 9.2 9.3 9.3.1 9.4 Mean Squared Error-----66 R2 - Intuition------- 67 R2 - Computation — 70 Calculating/?2------- 72 Interpreting/?2------- 73 9.5 Summary----- 74 10 10.1 10.2 10.3 10.4 10.5 Model Assessment - Classification — 75 Accuracy-----75 Confusion Matrix-----76 Precision and Recall — 77 ROC/AUC Curve------79 Summary — 81 Part III: Machine Learning II 11 11.1 11.2 11.3 11.4 11.5 12 12.1 Multiple Linear Regression - Concept — 85 Bird’s Eye View------ 85 Multiple Regression Workflow------88 Case Study: Does Sleep Improve Academic Performance? Interpreting the Regression Equation-----95 Summary — 96 Multiple Linear Regression - Theory — 97 Standardized Coefficients — 97
Contents — XI ձ2,2 12,3 Multiple Linear Regression Diagnostics Checklist---- 100 Summary----- 106 13 Multiple Linear Regression - Practice---- 108 14 14.1 14.2 14.3 14.4 Logistic Regression - Concept---- 111 Bird’s Eye View---- 111 Probabilities and Decision Making---- 112 Case Study: Credit Card Payments---- 114 Summary---- 118 15 15.1 15.2 15.3 15.4 15.5 15.6 Logistic Regression - Theory---- 119 Logistic Regression Function----119 Odds, Odds Ratio, and Logit---- 122 Logistic Regression Equation in Logit Form---- 123 Interpreting the Coefficients-----124 Derivation of the Logistic Regression Equation as Log Odds---- 125 Summary---- 126 16 Logistic Regression - Practice 17 17.1 17.2 17.3 /f-Means - Concept-----131 Bird’s Eye View---- 131 Case Study: Marketing Segmentation---- 133 Summary----- 135 18 18.1 18.2 18.3 ff-Means - Theory----- 136 Expectation - Maximization----- 136 Choice of К---- 139 Summary-----142 19 ff-Means - Practice-----144 127 Part IV: Deep Learning 20 20.1 20.2 20.3 Deep Learning - Bird’s Eye View---- 149 Foundational Principles----- 149 From Evidence to Judgment---- 150 Summary-----152 21 Neurons---- 154
XII ----- Contents 21.1 Neurons as Functions----- 154 21.1.1 Activation Function Computation----- 158 21.2 Mathematics of Neural Computation----- 158 21.3 Linear Algebra for Neural Computation----- 159 21.4 Neural Computation in Matrix Form----- 162 21.5 Summary----- 163 22 Neurons - Practice----- 164 23 Network Architecture----- 167 23.1 Layers----- 167 23.2 Parameters----- 168 23.3 Hyperparameters---- 171 23.4 Summary----- 171 24 Network Architecture - Practice —172 25 Forward Propagation —174 25.1 Forward Propagation Flow----- 174 25.2 Function Composition----- 174 25.3 Forward Propagation Computation----- 176 25.4 Computation Worked Example------178 25.5 Summary----- 180 26 Forward Propagation - Practice —181 27 Loss Function----- 185 27.1 A Game of Arrows---- 185 27.2 Common Loss Functions in Deep Learning —186 27.3 Summary----- 190 28 Loss Function - Practice —191 29 Backward Propagation —194 29.1 Computation as Gears —194 29.2 Gradient Descent-----195 29.3 Gradient Descent for Simple Linear Regression----- 197 29.4 Gradients and Backpropagation —199 29.5 Summary----- 200 30 Backward Propagation - Practice — 201
Contents 31 Deep Learning-Practice----- 205 List of Figures----- 209 List of Tables----- 215 About the Authors----- 217 Index----- 219 ХШ
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| adam_txt |
Contents Acknowledgments---- VII Preface — XV 1 1.1 լ.շ 1.3 1.4 Introduction---- 1 Artificial Intelligence---- 1 What Is Machine Learning?------ 3 Areas of Machine Learning----- 4 Machine Learning Workflow------5 Part I: Machine Learning I 2 2.1 2.2 2.3 2.4 2.5 2.6 Simple Linear Regression - Concept — 9 Bird’s Eye View---- 9 Fundamental Equation of SLR---- 14 Some Assumptions of Simple Linear Regression---- 15 Case Study: Is Earning Related to Learning?---- 17 Interpreting Regression---- 20 Summary---- 22 3 3.1 3.2 3.3 3.4 3.5 3.6 Simple Linear Regression - Theory — 23 Some Basics of Measuring Distance---- 23 Total Absolute Error (TAE)---- 26 Residual Sum of Squares (RSS)--- 27 Mean Squared Error---- 28 Analytical vs. Numerical Solutions in Machine Learning Summary — 30 4 Simple Linear Regression - Practice — 31 5 5.1 5.2 5.3 5.4 К-Nearest Neighbors (KNN) - Concept---- 34 Bird’s Eye View---- 34 Prediction Structure---- 35 Case Study: To Loan or Not to Loan---- 38 Summary---- 42 6 6.1 6.2 К-Nearest Neighbors (KNN) - Theory---- 43 Distance Metrics---- 43 Choosing к in KNN---- 50
Λ ------ 6.3 6.4 7 Contents ML Pipelines, Hyperparameters-----52 Summary-----52 К-Nearest Neighbors (KNN) - Practice — 53 Partii: Model Assessment 8 8.1 8.2 8.3 8.4 8.5 9 Model Assessment - Bias-Variance Tradeoff — 59 Train-Test Split-----59 /Г-Fold Cross-Validation-----60 Underfit and Overfit-----60 Bias-Variance Tradeoff — 64 Summary----- 65 Model Assessment-Regression — 66 9.1 9.2 9.3 9.3.1 9.4 Mean Squared Error-----66 R2 - Intuition------- 67 R2 - Computation — 70 Calculating/?2------- 72 Interpreting/?2------- 73 9.5 Summary----- 74 10 10.1 10.2 10.3 10.4 10.5 Model Assessment - Classification — 75 Accuracy-----75 Confusion Matrix-----76 Precision and Recall — 77 ROC/AUC Curve------79 Summary — 81 Part III: Machine Learning II 11 11.1 11.2 11.3 11.4 11.5 12 12.1 Multiple Linear Regression - Concept — 85 Bird’s Eye View------ 85 Multiple Regression Workflow------88 Case Study: Does Sleep Improve Academic Performance? Interpreting the Regression Equation-----95 Summary — 96 Multiple Linear Regression - Theory — 97 Standardized Coefficients — 97
Contents — XI ձ2,2 12,3 Multiple Linear Regression Diagnostics Checklist---- 100 Summary----- 106 13 Multiple Linear Regression - Practice---- 108 14 14.1 14.2 14.3 14.4 Logistic Regression - Concept---- 111 Bird’s Eye View---- 111 Probabilities and Decision Making---- 112 Case Study: Credit Card Payments---- 114 Summary---- 118 15 15.1 15.2 15.3 15.4 15.5 15.6 Logistic Regression - Theory---- 119 Logistic Regression Function----119 Odds, Odds Ratio, and Logit---- 122 Logistic Regression Equation in Logit Form---- 123 Interpreting the Coefficients-----124 Derivation of the Logistic Regression Equation as Log Odds---- 125 Summary---- 126 16 Logistic Regression - Practice 17 17.1 17.2 17.3 /f-Means - Concept-----131 Bird’s Eye View---- 131 Case Study: Marketing Segmentation---- 133 Summary----- 135 18 18.1 18.2 18.3 ff-Means - Theory----- 136 Expectation - Maximization----- 136 Choice of К---- 139 Summary-----142 19 ff-Means - Practice-----144 127 Part IV: Deep Learning 20 20.1 20.2 20.3 Deep Learning - Bird’s Eye View---- 149 Foundational Principles----- 149 From Evidence to Judgment---- 150 Summary-----152 21 Neurons---- 154
XII ----- Contents 21.1 Neurons as Functions----- 154 21.1.1 Activation Function Computation----- 158 21.2 Mathematics of Neural Computation----- 158 21.3 Linear Algebra for Neural Computation----- 159 21.4 Neural Computation in Matrix Form----- 162 21.5 Summary----- 163 22 Neurons - Practice----- 164 23 Network Architecture----- 167 23.1 Layers----- 167 23.2 Parameters----- 168 23.3 Hyperparameters---- 171 23.4 Summary----- 171 24 Network Architecture - Practice —172 25 Forward Propagation —174 25.1 Forward Propagation Flow----- 174 25.2 Function Composition----- 174 25.3 Forward Propagation Computation----- 176 25.4 Computation Worked Example------178 25.5 Summary----- 180 26 Forward Propagation - Practice —181 27 Loss Function----- 185 27.1 A Game of Arrows---- 185 27.2 Common Loss Functions in Deep Learning —186 27.3 Summary----- 190 28 Loss Function - Practice —191 29 Backward Propagation —194 29.1 Computation as Gears —194 29.2 Gradient Descent-----195 29.3 Gradient Descent for Simple Linear Regression----- 197 29.4 Gradients and Backpropagation —199 29.5 Summary----- 200 30 Backward Propagation - Practice — 201
Contents 31 Deep Learning-Practice----- 205 List of Figures----- 209 List of Tables----- 215 About the Authors----- 217 Index----- 219 ХШ |
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| spelling | Essa, Alfred Verfasser (DE-588)1254812377 aut Practical AI for business leaders, product managers, and entrepreneurs Alfred Essa and Shirin Mojarad Boston ; Berlin De Gruyter [2022] XVII, 221 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Business and economics Unternehmen (DE-588)4061963-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Unternehmen (DE-588)4061963-1 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Mojarad, Shirin Verfasser (DE-588)1254810056 aut Erscheint auch als Online-Ausgabe, PDF 978-1-5015-0573-7 Erscheint auch als Online-Ausgabe, EPUB 978-1-5015-0584-3 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033308103&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Essa, Alfred Mojarad, Shirin Practical AI for business leaders, product managers, and entrepreneurs Unternehmen (DE-588)4061963-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4061963-1 (DE-588)4033447-8 |
| title | Practical AI for business leaders, product managers, and entrepreneurs |
| title_auth | Practical AI for business leaders, product managers, and entrepreneurs |
| title_exact_search | Practical AI for business leaders, product managers, and entrepreneurs |
| title_exact_search_txtP | Practical AI for business leaders, product managers, and entrepreneurs |
| title_full | Practical AI for business leaders, product managers, and entrepreneurs Alfred Essa and Shirin Mojarad |
| title_fullStr | Practical AI for business leaders, product managers, and entrepreneurs Alfred Essa and Shirin Mojarad |
| title_full_unstemmed | Practical AI for business leaders, product managers, and entrepreneurs Alfred Essa and Shirin Mojarad |
| title_short | Practical AI for business leaders, product managers, and entrepreneurs |
| title_sort | practical ai for business leaders product managers and entrepreneurs |
| topic | Unternehmen (DE-588)4061963-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Unternehmen Künstliche Intelligenz |
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