<ul data-eligibleForWebStory="false"><li>Neural Networks are machine learning models inspired by the human brain, made of layers of neurons that process data.</li><li>Each neuron in a Neural Network takes inputs, weights them, adjusts flexibility with bias, and uses activation functions to determine the output.</li><li>Weights in a Neural Network decide how much importance each input holds, while bias serves as a small adjustment, akin to a backup.</li><li>Forward Propagation involves data moving through the network to produce output, while Backward Propagation allows the model to learn, adjust weights and biases, and reduce errors over time.</li></ul>

Neural Networks Explained Simply — Part 1

Discover more