The Theory and Practice of Enterprise AI : Recipes and Reference Implementations for Marketing, Supply Chain, and Production Operations

Ilya Katsov

ISBN 10: 0578328623 / ISBN 13: 9780578328621

Published by Grid Dynamics, 2022

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive guide to how marketing, supply chain, and production operations can be improved using deep learning and reinforcement learning methods, as well as their combination with traditional analytics and optimization approaches. The book is written for enterprise data scientists and analytics managers, and will also be useful for graduate students in operations research and applied statistics.The Theory and Practice of Enterprise AI is divided into five parts. Part I introduces basic concepts of enterprise decision automation and presents relevant deep and reinforcement learning methods. Part II presents recipes for customer analytics and personalization. Part III describes search and recommendation solutions that extensively leverage content data such as texts and images. Part IV discusses methods for price optimization and inventory management. Finally, Part V presents blueprints for anomaly detection and visual inspection that help to improve production and transportation operations. Python code examples are provided in the complementary online repository to support the reader's understanding of the implementation details.

Seller Inventory # 9780578328621

Bibliographic Details

• Title: The Theory and Practice of Enterprise AI : Recipes and Reference Implementations for Marketing, Supply Chain, and Production Operations
• Publisher: Grid Dynamics
• Publication Date: 2022
• Language: English
• ISBN 10: 0578328623
• ISBN 13: 9780578328621
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 157H x 35D millimeters 235 millimeters width by 157 millimeters height by 35 millimeters depth
• Weight: 974 grams

About this title

Synopsis

This book provides a comprehensive guide to how marketing, supply chain, and production operations can be improved using deep learning and reinforcement learning methods, as well as their combination with traditional analytics and optimization approaches. The book is written for enterprise data scientists and analytics managers, and will also be useful for graduate students in operations research and applied statistics.

The Theory and Practice of Enterprise AI is divided into five parts. Part I introduces basic concepts of enterprise decision automation and presents relevant deep and reinforcement learning methods. Part II presents recipes for customer analytics and personalization. Part III describes search and recommendation solutions that extensively leverage content data such as texts and images. Part IV discusses methods for price optimization and inventory management. Finally, Part V presents blueprints for anomaly detection and visual inspection that help to improve production and transportation operations. Python code examples are provided in the complementary online repository to support the reader's understanding of the implementation details.