Backpropagation
Backpropagation is a method used in training artificial neural networks. It works by calculating the error between the predicted output and the actual output, then propagating this error backward through the network. Each connection’s contribution to the error is assessed, allowing the network to adjust its weights or connections to improve future predictions. Essentially, it’s like a feedback system: the network learns from its mistakes, gradually refining its processes to enhance accuracy over time, much like how we learn from experiences. This iterative approach is fundamental to enabling machines to learn patterns and make decisions.
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Backpropagation is a method used in training neural networks, which are computer systems modeled after the human brain. When the network makes a prediction, backpropagation compares the prediction to the actual result, calculating the error. It then works backward through the network, adjusting the connections (or weights) between nodes to reduce this error for future predictions. This process is akin to learning from mistakes, as it helps the network improve its accuracy over time by fine-tuning how it processes information. Essentially, backpropagation enables machines to learn from their experiences.