How do neural networks learn?
By comparing predictions to actual labels using a loss function, and updating weights using backpropagation and gradient descent.
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More FAQs in What is a Neural Network? Deep Learning Foundations Explained
A perceptron is the simplest form of a neural network, consisting of a single neuron with inputs, weights, a bias, and an output.
Without activation functions, a neural network is just a giant linear equation, making it incapable of learning complex non-linear patterns.
Rectified Linear Unit (ReLU) is an activation function defined as f(x) = max(0, x). It helps speed up training and prevents vanishing gradients.
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