🧠 01 — Neural Network Intuition¶
A neural network is a stack of layers that learns transformations from inputs to outputs.
Basic Components¶
| Component | Meaning |
|---|---|
| Neuron | computes weighted sum + activation |
| Layer | group of neurons |
| Weight | learned importance value |
| Bias | learned offset |
| Activation | nonlinear function |
| Loss | error measure |
Why Nonlinearity Matters¶
Without activation functions, stacked layers behave like one linear model.
Common activations:
- ReLU
- sigmoid
- tanh
- softmax
Use Cases¶
- image classification
- NLP
- speech
- recommendation systems
- complex nonlinear prediction
For small tabular data, tree models often beat neural networks.