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Enterprise AI for Dummies

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Jarvinen, Zachary
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2020.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Jarvinen, Zachary. 
245 1 0 |a Enterprise AI for Dummies  |h [electronic resource]. 
260 |a Newark :  |b John Wiley & Sons, Incorporated,  |c 2020. 
300 |a 1 online resource (355 p.) 
500 |a Description based upon print version of record. 
505 0 |a Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Strong, Weak, General, and Narrow -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Exploring Practical AI and How It Works -- Chapter 1 Demystifying Artificial Intelligence -- Understanding the Demand for AI -- Converting big data into actionable information -- Relieving global cost pressure -- Accelerating product development and delivery -- Facilitating mass customization -- Identifying the Enabling Technology -- Processing -- Algorithms 
505 8 |a Data -- Storage -- Discovering How It Works -- Semantic networks and symbolic reasoning -- Text and data mining -- Machine learning -- Auto-classification -- Predictive analysis -- Deep learning -- Sentiment analysis -- Chapter 2 Looking at Uses for Practical AI -- Recognizing AI When You See It -- ELIZA -- Grammar check -- Virtual assistants -- Chatbots -- Recommendations -- Medical diagnosis -- Network intrusion detection and prevention -- Fraud protection and prevention -- Benefits of AI for Your Enterprise -- Healthcare -- Manufacturing -- Energy -- Banking and investments -- Insurance 
505 8 |a Retail -- Legal -- Human resources -- Supply chain -- Transportation and travel -- Telecom -- Public sector -- Professional services -- Marketing -- Media and entertainment -- Chapter 3 Preparing for Practical AI -- Democratizing AI -- Visualizing Results -- Comparison -- Composition -- Distribution -- Relationship -- Digesting Data -- Identifying data sources -- Cleaning the data -- Defining Use Cases -- A → B -- Good use cases -- Bad use cases -- Reducing bias -- Choosing a Model -- Unsupervised learning -- Supervised learning -- Deep learning -- Reinforcement learning 
505 8 |a Chapter 4 Implementing Practical AI -- The AI Competency Hierarchy -- Data collection -- Data flow -- Explore and transform -- Business intelligence and analytics -- Machine learning and benchmarking -- Artificial intelligence -- Scoping, Setting Up, and Running an Enterprise AI Project -- Define the task -- Collect the data -- Prepare the data -- Build the model -- Test and evaluate the model -- Deploy and integrate the model -- Maintain the model -- Creating a High-Performing Data Science Team -- The Critical Role of Internal and External Partnerships -- Internal partnerships 
505 8 |a External partnerships -- The importance of executive buy-in -- Weighing Your Options: Build versus Buy -- When you should do it yourself -- When you should partner with a provider -- Hosting in the Cloud versus On Premises -- What the cloud providers say -- What the hardware vendors say -- The truth in the middle -- Part 2 Exploring Vertical Market Applications -- Chapter 5 Healthcare/HMOs: Streamlining Operations -- Surfing the Data Tsunami -- Breaking the Iron Triangle with Data -- Matching Algorithms to Benefits -- Examining the Use Cases -- Delivering lab documents electronically 
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650 6 |a Intelligence artificielle. 
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