Deep Learning Fundamentals

The Core Idea

Deep learning relies on representing data across layered feature spaces.

These layers automatically extract increasingly complex patterns from the raw input.

Neural Networks Explained

At its heart, deep learning uses artificial neural networks – inspired by the human brain.

These networks consist of interconnected nodes arranged in layers, processing information as it passes through.

live demo · related simulation● LIVE

Training the Networks

Deep learning models learn through a process called training, where they are fed large amounts of data.

The model adjusts its internal parameters – known as weights – to minimize errors and improve accuracy.

Frequently asked questions

What is deep learning?

Deep learning is a family of machine learning methods that use multi-layer neural networks.

How do neural networks learn?

Neural networks learn by adjusting the connections between their nodes based on feedback during training, minimizing errors over time.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation