Overview

This book sets the reader on the path to establishing a data analytics strategy which includes artificial intelligence with a common language and capabilities with usages. A capability maturity framework with enterprise infrastructure is included as also an assessment tool for measuring and managing it to higher levels of maturity.

Full Product Details

Author:   Esther Baldwin
Publisher:   De Gruyter
Imprint:   De|G Press
ISBN:  

9781501515859

ISBN 10:   1501515853
Pages:   260
Publication Date:   18 November 2019
Audience:   General/trade ,  General ,  General
Format:   Paperback
Publisher's Status:   Postponed Indefinitley
Availability:   Temporarily unavailable  

Table of Contents

Table of Contents 1. Introduction What is Artificial Intelligence? Why is it important? Analytics Maturity Curve 2. AI Usages Business Cases: Automotive Retail Manufacturing Health Finance and Banking Academia and Education ..... AI for Good 3. AI Infrastructure Algorithms - the top 10 Hardware Software: frameworks Summary 4. AI Capability 5. Analytics Assessment Self-Assessment Process Assessment Teams The Assessment Process The Self-Assessment Framework Section 1. Management Commitment Section 2. Business Responsibility Section 3. AI Competency Section 4. Linking it to your Values - AI for good Scoring the Self Assessment Developing an Action Plan Moving to the Next Maturity Level - from spreadsheets to prediction? AI Support Impact of AI The Importance of Assessment Summary 6. Launching AI - Getting Started Gaining Management Commitment Create and Communicate a Vision Develop an Innovation Strategy Measure Business Value Developing Innovation Capabilities Skills - make or buy Training Assets, Methods, Skillsets Develop Infrastructure Enablers and Inhibitors Summary 7. Deep Learning (DL) DL in action DL history DL vs classical ML DL frameworks DL in the Cloud - case study Neon introduction 8. AI Basics Multi-layer perceptions (MLP) Activations Initializations Costs Optimizers - Gradient descent and variants Backpropagation Hands on exercise 9. Convolutional Neural Networks CNN overview / examples Layers Convolutions Pooling Dropout Local response normalization De-conv Popular CNNs Hands on exercise Appendix A Case Studies Appendix B Assessment Tool Self-Assessment Tool Section 1. Management Commitment Section 2. Business Responsibility Section 3. Competency Section 4. Enterprise Values Section 5. Support Section 6. Impact Appraising Performance Appendix C hands on examples - links and code References

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Esther Baldwin, Artificial Intelligence Strategist, Intel, USA

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