Feedback Loops

Feedback loops refer to a process in which the output of a system is returned to the input, allowing the system to regulate itself. In data science and artificial intelligence, feedback loops are used to improve the performance of algorithms and models. By analyzing the output of a model and comparing it to the desired output, adjustments can be made to the input data or the model itself to improve accuracy and reduce errors. Feedback loops are also used in machine learning to adjust the weights and biases of neural networks, allowing the model to learn and improve over time. In addition, feedback loops are used in data-driven decision making, where the results of a decision are analyzed and used to adjust future decisions, creating a continuous improvement cycle.
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