Understanding Neural Networks
How artificial neurons, layers, and activation functions combine to learn complex patterns.
Inspired by learning material from Google
Neural networks are the engine behind modern AI. They're loosely inspired by how the brain processes information.
The artificial neuron
Each neuron takes inputs, multiplies them by weights, adds a bias, and passes the result through an activation function. This produces an output that feeds into the next layer.
Layers
- Input layer: receives your data.
- Hidden layers: transform the data into useful representations.
- Output layer: produces the final prediction.
Networks with many hidden layers are called deep networks.
Activation functions
Functions like ReLU and sigmoid add non-linearity, letting the network model complex relationships that a straight line never could.
How it learns: backpropagation
The network compares its output to the truth, computes the error, and propagates that error backward to adjust every weight — a process called backpropagation, repeated millions of times.
Key takeaways
- Neurons combine weighted inputs and an activation function.
- Depth lets networks learn rich, layered patterns.
- Backpropagation is how the network improves.