Deep Learning Fundamentals

The Core Idea

Deep learning relies on representing data across layered feature spaces.

These layers allow the system to learn increasingly complex patterns from raw input.

Neural Networks: The Building Blocks

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

These networks consist of interconnected nodes that process information and learn from it.

live demo · related simulation● LIVE

Training the Networks

Deep learning models are ‘trained’ using vast amounts of data, adjusting their internal parameters to minimize errors.

This iterative process allows them to accurately predict outcomes or classify information.

Frequently asked questions

What is deep learning?

Deep learning is a family of machine learning methods that use multi-layer neural networks to analyze data and make predictions.

How do neural networks learn?

Neural networks learn by adjusting the strength of connections between their nodes, based on feedback during training – a process called backpropagation.

▶ Try it live

Everything above runs in your browser — open Wave Interference in a Double Slit and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Wave Interference in a Double Slit simulation