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 adjust their connections based on training data.

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Training the Network

The network learns through a process called ‘training,’ where it’s fed with large amounts of data.

It then 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.

What are neural networks?

Neural networks are computational models inspired by the structure and function of biological neurons, designed to recognize patterns in data.

How does training a neural network work?

Training involves feeding the network labeled data and adjusting its internal parameters – weights – to minimize the difference between predicted outputs and actual values.

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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.

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