Part I: Foundations of Learning and Complexity
Understanding the theoretical bedrock of machine learning
Overview
Part I establishes the theoretical foundations that underpin all of deep learning. Before diving into neural network architectures, we must understand why certain approaches work—and the answer lies in information theory, complexity, and compression.
Chapters
| # | Chapter | Key Concept |
|---|---|---|
| 1 | The Minimum Description Length Principle | Best model = shortest description |
| 2 | Kolmogorov Complexity | Complexity = shortest program length |
| 3 | Keeping Neural Networks Simple | Training = compression |
| 4 | The Coffee Automaton | Complexity rises then falls |
| 5 | The First Law of Complexodynamics | Interestingness is inevitable |
The Big Picture
MDL + Kolmogorov → Why simplicity matters
↓
Hinton's Paper → How this applies to neural nets
↓
Coffee Automaton + Complexodynamics → Why learning is even possible
Key Takeaway
Learning is compression. Intelligence finds patterns. The universe creates interesting structures on its way from order to chaos—and neural networks exploit this fact.
Prerequisites
- Basic probability and statistics
- Familiarity with information theory concepts (bits, entropy)
- Curiosity about “why things work”
What You’ll Be Able To Do After Part I
- Explain why regularization works from first principles
- Understand the deep connection between compression and learning
- Appreciate why neural networks can generalize
- Think about AI from a theoretical perspective