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Backpropagation is a fundamental concept in both AI and machine learning. It's a method used in training neural networks by adjusting the weights of the nodes based on the error rate obtained in the previous epoch (iteration). The main difference lies in the application. In AI, backpropagation is used to enable the system to learn from its mistakes and improve its predictions over time. In machine learning, it's used to optimize the model's performance by minimizing the error rate. However, it's important to note that AI and machine learning often overlap, and backpropagation is a common technique in both.
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The way the AI teaches itself how to weight each input is called backpropagation. Developers give the AI training examples or inputs where the desired output is known. It creates predictions from this, and an error score is assigned to each output. The machine then rebalances itself backwards over time to learn the optimal weights to minimize errors and make more and more accurate predictions. And that's generally how AI works.
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