Neural networks mimic the human brain to learn complex patterns from data. They are the building blocks of modern deep learning systems.
Key Components
- Input, hidden, and output layers
- Activation functions like ReLU and sigmoid
- Backpropagation and gradient descent
Why They Work
Neural networks learn by adjusting weights through many small updates during training, allowing them to model non-linear relationships.
Getting Started
Use libraries like TensorFlow or PyTorch to build and train your first network on image or text data.