Learning Rule in ML - Python and Machine Learning for Integrated Circuits - - An Online Book - |
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Python and Machine Learning for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In machine learning, a learning rule refers to a mathematical algorithm or procedure that adjusts the parameters of a machine learning model in response to the input data, with the goal of improving the model's performance on a specific task. Learning rules are a fundamental component of supervised learning, where a model learns from labeled training data, and they are typically used to update the model's parameters (e.g., weights in a neural network or coefficients in a linear regression model). The learning rule is responsible for minimizing the difference between the model's predictions and the true target values in the training data. This difference is often expressed as a loss or cost function, and the learning rule's objective is to minimize this cost function by iteratively adjusting the model's parameters. Common learning rules in machine learning include:
The choice of learning rule depends on the specific machine learning task, the type of model being used, and the available data. The goal is to find the optimal set of model parameters that minimize the difference between the predicted values and the true values for the training data, while avoiding overfitting to ensure good generalization to unseen data. Table 3860. Examples of applications of learning rules.
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