Course path

One direct route through the handbook. Open a lesson, complete its small experiment, then return here for the next step.

0. Start with the big picture

Introduction and AI landscape map

1. Understand generalization

1. Train, Validation, and Test Sets
2. Generalization
3. Bias and Variance
4. Regularization

2. Evaluate honestly

5. Data Leakage
6. Metrics

3. Learn a binary decision

7. The Perceptron
8. From Bernoulli to Log Loss
9. Loss and Optimization

4. Understand multi-step learning

10. Backpropagation

5. Forecast through time

11. Forecasting Baselines
12. Rolling-Origin Backtesting
13. Probabilistic Forecasting

After the forecasting concepts, compare the model path:

TCN → ModernTCN → Chronos

Reference sheets

• Activation Functions
• Derivatives and Gradients
• Loss Functions
• Log Loss Formula Sheet
• Deep Learning Essential Formulas