The Foundation

How Neural Networks Actually Work

Understand the building blocks: neurons, layers, activation functions, loss, gradients, and backpropagation.

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Concepts in This Path

Neural Network

A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

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Illustrated Concept

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Activation Function

A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.

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Loss Function

A function measuring the difference between model predictions and true values, guiding the training process.

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Gradient Descent

An optimization algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

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Backpropagation

The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.

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Learning Rate

A hyperparameter controlling the step size in gradient descent - too high causes instability, too low slows convergence.

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