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The agent is rewarded or penalized to some extent for a correct or a mistaken reply, and on the basis of the positive reward points gained the model trains itself. And once more as soon as trained it gets ready to predict the new knowledge presented to it. Suppose we present images of apples, bananas and mangoes to the model, so what it does is, based mostly on some patterns and relationships, it creates clusters and divides the dataset into these clusters. Now if a new knowledge is fed to the mannequin, it adds it to one of the created clusters.<br><br>
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Machine Learning Training The agent is rewarded or penalized to some extent for a correct or a mistaken reply, and on the basis of the positive reward points gained the model trains itself. And once more as soon as trained it gets ready to predict the new knowledge presented to it. Suppose we present images of apples, bananas and mangoes to the model, so what it does is, based mostly on some patterns and relationships, it creates clusters and divides the dataset into these clusters. Now if a new knowledge is fed to the mannequin, it adds it to one of the created clusters.
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