With A Tail of Cat Meals Preferences
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16 hours in the past
Welcome to the ‘Braveness to study ML’. This sequence goals to simplify advanced machine studying ideas, presenting them as a relaxed and informative dialogue, very similar to the partaking fashion of “The Braveness to Be Disliked,” however with a give attention to ML.
On this installment of our sequence, our mentor-learner duo dives right into a contemporary dialogue on statistical ideas like MLE and MAP. This dialogue will lay the groundwork for us to realize a brand new perspective on our earlier exploration of L1 & L2 Regularization. For an entire image, I like to recommend studying this submit earlier than studying the fourth a part of ‘Braveness to Be taught ML: Demystifying L1 & L2 Regularization’.
This text is designed to sort out elementary questions which may have crossed your path in Q&A mode. As all the time, if you end up have related questions, you’ve come to the correct place:
What precisely is ‘chance’?The distinction between chance and probabilityWhy is chance vital within the context of machine studying?What’s MLE (Most Probability Estimation)?What’s MAP (Most A Posteriori Estimation)?The distinction between MLE and Least squareThe Hyperlinks and Distinctions Between MLE and MAP
Probability, or extra particularly the chance perform, is a statistical idea used to judge the chance of observing the given information beneath numerous units of mannequin parameters. It’s known as chance (perform) as a result of it’s a perform that quantifies how doubtless it’s to look at the present information for various parameter values of a statistical mannequin.
The ideas of chance and chance are essentially completely different in statistics. Chance measures the prospect of observing a selected final result sooner or later, given identified parameters or distributions…