What is gradient descent in neural networks? How do neural networks work? I do not have background in calculus.
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Neural networks are computational systems inspired by biological brains. They consist of interconnected layers of nodes, similar to neurons. Information flows from input layer through hidden layers to output layer. Each connection has a weight that determines how much influence one node has on another. The network processes data by combining inputs with these weights to make predictions or classifications.
Neural networks learn through a process of trial and error. Initially, they make random predictions. For example, when shown a cat image, the network might incorrectly predict it's a dog. By comparing predictions to correct answers, the network identifies its mistakes. These errors provide feedback that guides the network to adjust its internal parameters, gradually improving accuracy through repeated practice with many examples.
To understand how neural networks learn, imagine you're blindfolded at the top of a hill and need to reach the bottom. You can only feel the slope under your feet. By taking steps in the steepest downward direction, you gradually descend toward the valley bottom. This hill represents the network's error landscape - high points mean many mistakes, while the bottom represents perfect accuracy. This process of following the steepest descent is called gradient descent.
Gradient descent works through a systematic process. First, the network calculates its current error by comparing predictions to correct answers. Then it determines the gradient - the direction of steepest increase in error. The network moves in the opposite direction to decrease error. The learning rate controls step size: too large causes overshooting, too small means slow progress. This cycle repeats thousands of times, gradually adjusting network weights until errors are minimized.