Neural Networks are machine learning models inspired by the human brain, made of layers of neurons that process data.
Each neuron in a Neural Network takes inputs, weights them, adjusts flexibility with bias, and uses activation functions to determine the output.
Weights in a Neural Network decide how much importance each input holds, while bias serves as a small adjustment, akin to a backup.
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.