Practical AI for business leaders, product managers, and entrepreneurs

Media type: E-Book

Title: Practical AI for business leaders, product managers, and entrepreneurs

Contains: Frontmatter
Acknowledgments
Contents
Preface
1 Introduction
Part I: Machine Learning I
2 Simple Linear Regression – Concept
3 Simple Linear Regression – Theory
4 Simple Linear Regression – Practice
Part II: Model Assessment
8 Model Assessment – Bias-Variance Tradeoff
9 Model Assessment – Regression
10 Model Assessment – Classification
Part III: Machine Learning II
11 Multiple Linear Regression – Concept
12 Multiple Linear Regression – Theory
13 Multiple Linear Regression – Practice
14 Logistic Regression – Concept
15 Logistic Regression – Theory
16 Logistic Regression – Practice
Part IV: Deep Learning
20 Deep Learning – Bird’s Eye View
21 Neurons
22 Neurons – Practice
23 Network Architecture
24 Network Architecture – Practice
25 Forward Propagation
26 Forward Propagation – Practice
27 Loss Function
28 Loss Function – Practice
29 Backward Propagation
30 Backward Propagation – Practice
31 Deep Learning – Practice
List of Figures
List of Tables
About the Authors
Index

Contributor: Essa, Alfred [VerfasserIn]; Mojarad, Shirin [VerfasserIn]

imprint: Berlin; Boston: De Gruyter, [2022]

Extent: 1 Online-Ressource (XVII, 221 Seiten)

Language: English

DOI: 10.1515/9781501505737

ISBN: 9781501505737; 9781501505843

Identifier:

RVK notation: ST 610 : Wirtschaftswissenschaften
ST 300 : Allgemeines

Keywords: Künstliche Intelligenz > Python

Origination:

Footnote:

Description: Most economists agree that AI is a general purpose technology (GPT) like the steam engine, electricity, and the computer. AI will drive innovation in all sectors of the economy for the foreseeable future. Practical AI for Business Leaders, Product Managers, and Entrepreneurs is a technical guidebook for the business leader or anyone responsible for leading AI-related initiatives in their organization. The book can also be used as a foundation to explore the ethical implications of AI. Authors Alfred Essa and Shirin Mojarad provide a gentle introduction to foundational topics in AI. Each topic is framed as a triad: concept, theory, and practice. The concept chapters develop the intuition, culminating in a practical case study. The theory chapters reveal the underlying technical machinery. The practice chapters provide code in Python to implement the models discussed in the case study. With this book, readers will learn: • The technical foundations of machine learning and deep learning • How to apply the core technical concepts to solve business problems • The different methods used to evaluate AI models • How to understand model development as a tradeoff between accuracy and generalization • How to represent the computational aspects of AI using vectors and matrices • How to express the models in Python by using machine learning libraries such as scikit-learn, statsmodels, and keras

Access State: Restricted Access | Information to licenced electronic resources of the SLUB

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