Neural Networks Explained Simply
Neural networks power image and language AI. The core idea is simpler than the name suggests.
Layers of simple units
Inputs flow through layers where each unit adds up signals and passes a result on.
Learning by adjusting
During training the network compares its output with the right answer and adjusts connection weights to reduce error.
Why depth helps
More layers let the model build complex features from simple ones.
- Edges become shapes.
- Shapes become objects.
- Objects become scenes.
Limits
Networks need lots of data and can be hard to interpret.
- Hard to explain decisions.
- Can overfit.
- Costly to train.
Key takeaway: Neural networks learn by adjusting connection weights from examples.
