Practical AI for Business Leaders, Product Managers, and Entrepreneurs
Autor Shirin Mojarad, Alfred Essaen Limba Engleză Paperback – 4 apr 2022
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
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Specificații
ISBN-13: 9781501514647
ISBN-10: 1501514644
Pagini: 240
Ilustrații: 106 Illustrations, color; 85 Illustrations, black and white; 21 Tables, black and white
Dimensiuni: 170 x 240 x 15 mm
Greutate: 0.46 kg
Ediția:1. Auflage
Editura: De Gruyter
ISBN-10: 1501514644
Pagini: 240
Ilustrații: 106 Illustrations, color; 85 Illustrations, black and white; 21 Tables, black and white
Dimensiuni: 170 x 240 x 15 mm
Greutate: 0.46 kg
Ediția:1. Auflage
Editura: De Gruyter
Notă biografică
Introduction
What is AI and why it is at the center of major business transformation?
How is it related to machine learning?
What is deep learning, and how is it related to ML?
Why is it important?
How the book is organized
Who is the audience?
Section 1: Machine Learning Chapter 1.1, introduction, machine learning, different types of machine learning
Chapter 1.2, Machine Learning Technical Overview
Chapter 1.3, Hands-On Machine Learning with Scikit Learn
Chapter 1.4, Advanced Topics/flavors of Machine learning
Appendix: mathematical interlude
Section 2: Deep Learning
Chapter 2.1, introduction (what is it, why is it important)
Chapter 2.2, Deep Learning Technical Overview
Chapter 2.3, Hands-On Deep Learning with Keras
Chapter 2.4, Advanced Topics/flavors of deep learning
Appendix: mathematical interlude
Section 3: Putting AI into Practice: Innovation Framework
Chapter 3.1: Diffusion and Dynamics of Innovation
Chapter 3.2: Managing an Innovation Portfolio
What is AI and why it is at the center of major business transformation?
How is it related to machine learning?
What is deep learning, and how is it related to ML?
Why is it important?
How the book is organized
Who is the audience?
Section 1: Machine Learning Chapter 1.1, introduction, machine learning, different types of machine learning
Chapter 1.2, Machine Learning Technical Overview
Chapter 1.3, Hands-On Machine Learning with Scikit Learn
Chapter 1.4, Advanced Topics/flavors of Machine learning
Appendix: mathematical interlude
Section 2: Deep Learning
Chapter 2.1, introduction (what is it, why is it important)
Chapter 2.2, Deep Learning Technical Overview
Chapter 2.3, Hands-On Deep Learning with Keras
Chapter 2.4, Advanced Topics/flavors of deep learning
Appendix: mathematical interlude
Section 3: Putting AI into Practice: Innovation Framework
Chapter 3.1: Diffusion and Dynamics of Innovation
Chapter 3.2: Managing an Innovation Portfolio