Demystifying Generative AI: A Practical and Intuitive Introduction [Paperback]

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 IlÁ