Deep Learning Fundamentals

The Core Idea

Deep learning relies on representing data across layered feature spaces.

These layers allow the model to learn increasingly complex patterns and abstractions from raw input.

Neural Network Basics

A neural network consists of interconnected nodes, or neurons, organized in layers.

Each connection has a weight associated with it, which is adjusted during the learning process to improve performance.

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Forward and Backward Propagation

During forward propagation, input data flows through the network, producing an output.

Backward propagation uses the error between predicted and actual values to adjust the weights in a process called backpropagation.

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 through adjusting the weights of their connections based on feedback about the accuracy of their predictions, a process known as training.

What is backpropagation?

Backpropagation is an algorithm used to train neural networks by calculating the gradient of the loss function and using it to update the network's weights efficiently.

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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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