Definition
A neural network stacks layers of simple mathematical units (neurons) that transform inputs through weighted sums and nonlinear activations.
During training, weights adjust to minimize error on examples, the network learns representations useful for classification, generation, or prediction.
In simple terms
Imagine a series of filters in a photo app: each layer detects edges, then shapes, then objects. A neural network learns those filters automatically from data instead of hand-coding them.
Where you see it
- Image classifiers, speech recognizers, and LLMs are all neural networks.
- Recommendation systems use neural nets for ranking.
- Somali NLP models use neural encoders for text classification.
How it works
1.Forward pass
Input flows through layers; each neuron computes a weighted sum plus activation.
2.Loss calculation
Compare output to the correct answer; measure how wrong the prediction is.
3.Backpropagation
Compute gradients and update weights to reduce loss.
4.Repeat
Many epochs over the dataset until performance plateaus.
Why it matters
- Neural networks are the foundation of modern AI, from tiny classifiers to trillion-parameter LLMs.
Often confused
Neural networks mimic the brain accurately.
They are inspired by biology but mathematically are matrix operations optimized with calculus, useful analogy, not literal simulation.