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Practical AI for Business Leaders, Product Managers, and Entrepreneurs / Shirin Mojarad, Alfred Essa.

By: Contributor(s): Material type: TextTextPublisher: Berlin ; Boston : De Gruyter, [2022]Copyright date: ©2022Description: 1 online resource (XVIII, 222 p.)Content type:
Media type:
Carrier type:
ISBN:
  • 9781501514647
  • 9781501505843
  • 9781501505737
Subject(s): Other classification:
  • online - DeGruyter
Online resources: Available additional physical forms:
  • Issued also in print.
Contents:
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 -- 5 K-Nearest Neighbors (KNN) – Concept -- 6 K-Nearest Neighbors (KNN) – Theory -- 7 K-Nearest Neighbors (KNN) – 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 -- 17 K-Means – Concept -- 18 K-Means – Theory -- 19 K-Means – 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
Summary: 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
Holdings
Item type Current library Call number URL Status Notes Barcode
eBook eBook Biblioteca "Angelicum" Pont. Univ. S.Tommaso d'Aquino Nuvola online online - DeGruyter (Browse shelf(Opens below)) Online access Not for loan (Accesso limitato) Accesso per gli utenti autorizzati / Access for authorized users (dgr)9781501505737

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 -- 5 K-Nearest Neighbors (KNN) – Concept -- 6 K-Nearest Neighbors (KNN) – Theory -- 7 K-Nearest Neighbors (KNN) – 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 -- 17 K-Means – Concept -- 18 K-Means – Theory -- 19 K-Means – 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

restricted access online access with authorization star

http://purl.org/coar/access_right/c_16ec

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

Issued also in print.

Mode of access: Internet via World Wide Web.

In English.

Description based on online resource; title from PDF title page (publisher's Web site, viewed 01. Dez 2022)