Vor Ort
Merkzettel Warenkorb
Produktbild: Demystifying Generative AI: A Practical and Intuitive Introduction

Demystifying Generative AI: A Practical and Intuitive Introduction

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

07.01.2026

Verlag

Pearson Education Limited

Seitenzahl

448

Maße (L/B/H)

23,5/17,8/2,5 cm

Gewicht

774 g

Auflage

1

Sprache

Englisch

ISBN

978-0-13-542941-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

07.01.2026

Verlag

Pearson Education Limited

Seitenzahl

448

Maße (L/B/H)

23,5/17,8/2,5 cm

Gewicht

774 g

Auflage

1

Sprache

Englisch

ISBN

978-0-13-542941-9

Herstelleradresse

Financial Times Prent.
St.-Martin-Straße 82
81541 München
DE

Email: salesde@pearson.com

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: A Practical and Intuitive Introduction

  • 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