Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About this Book
About the Author
About the Cover
1. Fundamentals of deep learning
Chapter 1. What is deep learning?
1.1. Artificial intelligence, machine learning, and deep learning
1.1.1. Artificial intelligence
1.1.2. Machine learning
1.1.3. Learning representations from data
1.1.4. The “deep” in deep learning
1.1.5. Understanding how deep learning works, in three figures
1.1.6. What deep learning has achieved so far
1.1.7. Don’t believe the short-term hype
1.1.8. The promise of AI
1.2. Before deep learning: a brief history of machine learning
1.2.1. Probabilistic modeling
1.2.2. Early neural networks
1.2.3. Kernel methods
1.2.4. Decision trees, random forests, and gradient boosting machines
1.2.5. Back to neural networks
1.2.6. What makes deep learning different
1.2.7. The modern machine-learning landscape
1.3. Why deep learning? Why now?
1.3.1. Hardware
1.3.2. Data
1.3.3. Algorithms
1.3.4. A new wave of investment
1.3.5. The democratization of deep learning
1.3.6. Will it last?
