AI Foundations
Neural Networks
Neural networks, LLMs, and machine learning for markets.
In this lesson
- How a neural network learns
- What makes learning 'deep'
Key takeaways
- 1Networks adjust weights to reduce error in training
- 2'Deep' means many stacked layers
- 3More data and depth can capture richer patterns
Lesson summary
A neural network learns patterns by adjusting internal weights during training.
Mental model
What neural networks really means
A neural network learns patterns by adjusting internal weights during training. Deep networks stack many layers so they can model complex relationships.
- How a neural network learns
- What makes learning 'deep'
Mechanics
How to reason about neural networks
Training compares predictions with target outcomes and adjusts weights to reduce error.
Layers transform inputs into intermediate representations.
Performance depends on data quality, architecture, objective, and validation.
- Networks adjust weights to reduce error in training
- 'Deep' means many stacked layers
- More data and depth can capture richer patterns
Example
Neural Networks in practice
A network trained to classify market news may learn language patterns, but it still needs clean labels and testing on unseen examples.
Common mistakes
The usual neural networks trap
More layers or more parameters do not automatically mean better decisions. Bad data can make a larger model confidently wrong.
Risk notes
Before you rely on neural networks
Overfitting, biased data, leakage, and changing market regimes can make training results fail in production.
- Describe weights and training loss.
- Explain why layers matter.
- Check validation on unseen data.
Practice
A short drill for neural networks
- Describe weights and training loss.
- Explain why layers matter.
- Check validation on unseen data.
Review
Key terms
- Machine Learning
- Algorithms that learn patterns from data instead of being explicitly programmed.
- Neural Network
- A layered model loosely inspired by the brain, core to deep learning.
- Overfitting
- When a model memorizes noise and fails to generalize to new data.
- Large Language Model (LLM)
- An AI model trained to predict and generate text from context.
- Backtesting
- Testing a strategy on historical data before risking real capital.
Source notes
Editorial references
These references are starting points for verifying the mechanisms, risk checks, and product context behind this lesson.
Before you continue
Can you do these?
- Describe weights and training loss.
- Explain why layers matter.
- Check validation on unseen data.
Related learning
Keep reading
Checkpoint
Finish this lesson
Pass the check to save progress, then continue through the track in order.
Lock in this lesson
Answer every question correctly to complete the lesson.
A neural network learns by…