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With A Tail of Cat Meals Preferences
Welcome to the ‘Braveness to be taught ML’. This collection goals to simplify complicated machine studying ideas, presenting them as a relaxed and informative dialogue, very like the partaking type of “The Courage to Be Disliked,” however with a deal with ML.
On this installment of our collection, our mentor-learner duo dives right into a recent dialogue on statistical ideas like MLE and MAP. This dialogue will lay the groundwork for us to achieve a brand new perspective on our earlier exploration of L1 & L2 Regularization. For a whole image, I like to recommend studying this publish earlier than studying the fourth a part of ‘Courage to Learn ML: Demystifying L1 & L2 Regularization’.
This text is designed to sort out basic questions that may have crossed your path in Q&A method. As all the time, if you end up have related questions, you’ve come to the appropriate place:
- What precisely is ‘chance’?
- The distinction between chance and chance
- Why is chance necessary 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 sq.
- The Hyperlinks and Distinctions Between MLE and MAP
Probability, or extra particularly the chance perform, is a statistical idea used to guage the chance of observing the given information beneath numerous units of mannequin parameters. It’s referred to as chance (perform) as a result of it’s a perform that quantifies how doubtless it’s to watch the present information for various parameter values of a statistical mannequin.
The ideas of chance and chance are basically totally different in statistics. Chance measures the prospect of observing a particular end result sooner or later, given identified parameters or distributions…
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