Vor Ort
Merkzettel Warenkorb
Produktbild: Demystifying Generative AI

Demystifying Generative AI A Practical and Intuitive Introduction

55,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.01.2026

Verlag

Pearson Academic

Seitenzahl

448

Maße (L/B/H)

23,1/18,6/2,7 cm

Gewicht

776 g

Sprache

Englisch

ISBN

978-0-13-542941-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.01.2026

Verlag

Pearson Academic

Seitenzahl

448

Maße (L/B/H)

23,1/18,6/2,7 cm

Gewicht

776 g

Sprache

Englisch

ISBN

978-0-13-542941-9

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
Die Leseprobe wird geladen.
  • Produktbild: Demystifying Generative AI

  • Preface
    Part I The Foundations of Generative AI
    Chapter 1     Ten Breakthroughs That Made Generative AI Possible
    Breakthrough 1: The Turing Machine
    Breakthrough 2: The Artificial Neuron
    Breakthrough 3: The Dartmouth Conference
    Breakthrough 4: The Perceptron
    The Rise of Symbolic Reasoning (1960s)
    The First AI Winter (Early 1970s to Early 1980s)
    Breakthrough 5: Neural Networks and Backpropagation
    Breakthrough 6: Recurrent Neural Networks
    The Second AI Winter (Late 1980s to Mid-1990s)
    Breakthrough 7: Invention of the GPU
    Breakthrough 8: Reinforcement Learning
    Breakthrough 9: Language Modeling
    Breakthrough 10: The Transformer
    Summary
    References
    Chapter 2 The Machinery of Learning
    Types of Learning
    Supervised Learning
    Unsupervised Learning
    Reinforcement Learning
    The Machine Learning Family Tree
    What Is a Model?
    How Models Are Trained
    Training, Validation, and Test Datasets
    Inference Models
    How to Measure Model Accuracy
    Hyperparameters
    Summary
    Chapter 3 Foundational Algorithms
    Linear Regression: One Stroke to Represent the Data
    Describing a Line
    Loss Functions and Other Hyperparameters
    Classification
    Support Vector Machines
    Discovering Structures in Data
    K-Means, the Clustering King
    DBSCAN and Growing Clusters
    Summary
    Chapter 4 An Introduction to Neural Networks
    Neural Networks Key Concepts
    ANNs: General Structure and Terminology
    Training a Neural Network
    Training Models and Overcoming Challenges
    The Importance of Clean Data
    Labeled Data: The Backbone of Supervised Learning
    Avoiding the Pitfalls: Overfitting and Underfitting
    Scaling Up Training
    Summary
    Chapter 5 Neural Network Architectures
    Feedforward Neural Networks
    Traditional FFNs
    Convolutional Neural Networks (CNNs)
    Traditional Generative Models
    Generative Adversarial Networks (GANs)
    Variational Autoencoders (VAEs)
    Diffusion Models
    Recurrent Models
    Recurrent Neural Networks (RNNs)
    Long Short-Term Memory Networks (LSTMs)
    Summary
    Chapter 6 Reinforcement Learning: Teaching Machines to Learn by Trial and Error
    An AI That Learns Like Us
    Key Concepts of Reinforcement Learning
    The Markov Decision Process (MDP)
    The Bellman Equation
    Model-Based Versus Model-Free Systems
    On-Policy Versus Off-Policy Learning: Two Paths to Learning
    Monte Carlo Reinforcement Learning
    Temporal Difference (TD) Learning
    Q-Learning
    Deep Reinforcement Learning
    Summary
    References
    Part II The Generative AI Revolution
    Chapter 7     Language Modeling: The Birth of LLMs
    An Introduction to LLMs
    Foundations of Language Modeling
    Next-Word Prediction
    From Words to Tokens
    Word Embedding: Turning Tokens into Numbers
    How Word Embeddings Are Learned
    Semantic Relationships in the Embedding Space
    The Semantics of Language
    Summary
    Reference
    Chapter 8 Attention Is All You Need: The Foundation of Generative AI
    A New Architecture Begins to Take Shape
    Attention Is All You Need
    From Sequential to Parallel Processing
    Positional Encoding
    The Self-Attention Mechanism
    Summary
    References
    Chapter 9 Attention Isnt All You Need: Understanding the Transformer Architecture
    The Encoder Block
    The Multi-Head Attention Layer
    The Add and Norm Layers and Residual Connections
    The Feedforward Network (FFN) Layer
    Layers Upon Layers of Encoder Blocks
    How Encoders Are Trained
    The Decoder Block
    The Decoders Output Classifier
    How Decoders Are Trained
    What Type of Machine Learning Is Involved in Training LLMs?
    Case Study: The GPT-3 Transformer
    Future Directions
    Summary
    References
    Part III Living with Generative AI
    Chapter 10     Making Models Smarter: Prompt and Context Engineering
    Prompt and Context Windows
    Prompt Engineering Techniques
    Shot-Based Approaches
    Chain-Based Approaches
    Self-Ask Approaches
    Prompt Engineering Limitations
    Context Engineering
    Types of Contexts in LLM Workflows
    Tools and Protocols
    Context Design Techniques
    Summary
    Chapter 11 Retrieval-Augmented Generation
    The Need for RAG
    Common Applications of RAG
    RAG Trends and Practices
    The RAG Pipeline
    Query Formulation
    Retrieval Filtering
    Working with Knowledge Databases
    Loading Documents
    Chunking: Splitting Documents
    Embedding and Storing Segments
    Retrieving Segments
    Summary
    Chapter 12 Fine-Tuning LLMs
    The Need for Fine-Tuning
    Comparing Fine-Tuning and RAG
    Inference Hyperparameter Tuning for LLMs
    Temperature
    Top-K Sampling
    Top-P (Nucleus) Sampling
    Repetition Penalty
    Principles of Fine-Tuning with New Data
    Fine-Tuning for Model Types and Objectives
    Supervised Fine-Tuning (SFT)
    Transfer Learning
    Parameter-Efficient Fine-Tuning (PEFT) Methods
    Retrieval-Augmented Fine-Tuning (RAFT)
    Reinforcement Learning from Human Feedback (RLHF)
    Benchmarking Model Performance
    Summary
    References
    Chapter 13 Securing LLMs from Attack
    What Makes AI Security Different
    The Emergence and Importance of AI Security Frameworks
    NIST AI Risk Management Framework
    The OWASP Top 10
    MITRE ATLAS
    The ISO/IEC Suite of AI Standards
    A Comparison of the AI Security Frameworks
    AI Vulnerabilities and Attack Vectors
    Direct Prompt Injection Attacks
    Prompt Injections with Jailbreaking
    Indirect Prompt Injection Attacks
    Extraction and Inversion Attacks
    AI Supply Chain Threats
    Defending Models from Attack
    Extending the Guardrail System
    Architectural Safeguards
    Continuous Monitoring and Detection System
    Generative Adversarial Defense Techniques
    Summary
    References
    Chapter 14 AI Ethics and Bias: Building Responsible Systems
    Bias and Ethical Risks in GenAI
    The Biased Data That Shapes GenAI
    The Difficulty of Stopping GenAI Bias
    When Generative AI Goes Wrong: Unethical and Harmful Outputs
    Hallucination and Misinformation
    Synthetic Media, Deepfakes, and Disinformation
    Ownership, Consent, and Copyright
    Transparency, Explainability, and Trust
    Building Responsible Generative AI
    Alignment and AI Safety
    Practical Responses to Ethical AI Challenges
    Summary
    References
    Chapter 15 The Future of AI: From Generative to General Intelligence
    Where We Stand: A Snapshot of Todays Capabilities
    Current Limitations and Known Pain Points
    The Emergence Question: Are We Seeing Sparks of AGI?
    What Makes AGI Different?
    What Is AGI?
    What Is Intelligence Anyway?
    Do Reasoning LLMs Really Reason?
    Predicting Versus Understanding
    Are We Already on the Path to AGI?
    Paths to AGI
    The Scaling Hypothesis
    The Modular Hypothesis
    The Embodied System Hypothesis
    Hybrid Models
    Is AGI the End of Humanity?
    The Alignment Problem Revisited
    Black Boxes and Loss of Interpretability
    The Singularity and the Skynet Problem
    Controlling Existential Risks
    Is AGI Helping or Hurting Society?
    Will AI Take Your Job?
    Education in the Age of Generative AI
    Societal Identity and Stability
    The Evolving Voice of Generative AI
    From Single-Goal Prompting to Multimodal Partnering
    Responsive Interfaces
    Redefining Creativity
    The Future We Choose
    Scenario A: The Co-creative Society
    Scenario B: The Automated Present
    Scenario C: The Disrupted Path
    Summary
    References
    Appendix A The History of AI
    Appendix B A Summary of Neural Network Model Architectures
    Glossary

    9780135429419 TOC 12/19/2025