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Enterprise AI for Dummies
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Newark :
John Wiley & Sons, Incorporated,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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