Neural Networks

Neural networks, LLMs, and machine learning for markets.

Updated 2026 ж. 21 қаз.GaiaEx Academy Editorial

Бұл сабақта

How a neural network learns
What makes learning 'deep'

Негізгі тұжырымдар

1Networks adjust weights to reduce error in training
2'Deep' means many stacked layers
3More data and depth can capture richer patterns

Сабақ қорытындысы

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.

Есте сақтаңызDecision rule: Judge a model by out-of-sample behavior and error analysis, not by architecture size alone.

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.

Қайталау

Негізгі терминдер

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.

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