Premium
Title Page
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Epigraph
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Introduction
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Section 1: Building Your First AI Model
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Chapter 1.1: Introduction to AI and Its Applications
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Chapter 1.2: Fundamentals of Building an AI Model
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Chapter 1.3: Beginner's Guide to AI Model Development
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Chapter 1.4: AI Model Building for Healthcare Applications
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Chapter 1.5: AI Models for Financial Forecasting
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Chapter 1.6: Natural Language Processing Models
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Chapter 1.7: Computer Vision Model Development
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Chapter 1.8: AI Model Optimization Techniques
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Chapter 1.9: Tools and Frameworks for AI Model Building
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Chapter 1.10: Ethical Considerations in AI Model Development
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Chapter 1.11: Real-time AI Model Deployment Strategies
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Chapter 1.12: Custom AI Models for Small Businesses
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Section 2: Building Blocks of LLM
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Chapter 2.1: Introduction to Large Language Models
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Chapter 2.2: Data Collection and Preprocessing for LLMs
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Chapter 2.3: Model Architecture Design for LLMs
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Chapter 2.4: Fine-tuning Techniques for Domain-Specific LLMs
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Chapter 2.5: Ethical Considerations in LLM Development
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Chapter 2.6: Performance Evaluation Metrics for LLMs
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Chapter 2.7: Deployment Strategies for Scalable LLMs
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Chapter 2.8: Cost Management in Training Large Language Models
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Chapter 2.9: Open Source Tools and Frameworks for LLM Development
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Chapter 2.10: Managing Bias and Fairness in LLMs
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Chapter 2.11: Future Trends in LLM Research and Development
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Section 3: Generative AI Essentials
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Chapter 3.1: Introduction to Generative AI
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Chapter 3.2: Basics of Generative AI: Key Concepts and Terminology
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Chapter 3.3: Applications of Generative AI in Creative Industries
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Chapter 3.4: Ethical Considerations in Generative AI Development
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Chapter 3.5: Generative AI Tools and Software for Beginners
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Chapter 3.6: Understanding Machine Learning Models in Generative AI
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Chapter 3.7: Case Studies of Successful Generative AI Projects
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Chapter 3.8: Generative AI in Content Creation - Text, Images, and Beyond
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Chapter 3.9: The Future of Generative AI - Trends and Predictions
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Chapter 3.10: Practical Exercises for Learning Generative AI
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Chapter 3.11: Comparing Generative AI with Traditional AI Techniques
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Chapter 3.12: DeepSeek - The New Kid On The Block
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Section 4: Autonomous Systems Governance
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Chapter 4.1: Introduction to Autonomous Systems Governance
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Chapter 4.2: Models for Accountability in AI
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Chapter 4.3: Ethical and Transparent AI Systems
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Chapter 4.4: Regulatory Compliance in AI Systems
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Chapter 4.5: AI Bias Mitigation Strategies
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Chapter 4.6: Ethical Frameworks for AI Development
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Chapter 4.7: Transparency Standards for Machine Learning Models
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Chapter 4.8: Governance Models for Autonomous Systems
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Chapter 4.9: Accountability Mechanisms for AI Decision-Making
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Chapter 4.10: Risk Assessment Tools for AI Applications
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Chapter 4.11: Stakeholder Engagement in AI Ethics
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Chapter 4.12: Auditing Processes for AI Systems
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Chapter 4.13: Best Practices for Responsible AI Deployment
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About The Author
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