Explain the difference between supervised and unsupervised learning. Provide examples.

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Explain the difference between supervised and unsupervisedlearning. Provide examples.

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There are a few different ways to build IKEA furniture Eachwill ideally lead to a completed couch or chair But depending onthe details one approach will make more sense than the othersGot the instruction manual and all the right pieces Just followdirections Getting the hang of it Toss the manual aside and gosolo But misplace the instructions and its up to you to makesense of that pile of wooden dowels and planksIts the same with deep learning Based on the kind of dataavailable and the research question at hand a scientist willchoose to train an algorithm using a specific learning modelIn a supervised learning model the algorithm learns on alabeled dataset providing an answer key that the algorithm can useto evaluate its accuracy on training data An unsupervised modelin contrast provides unlabeled data that the algorithm tries tomake sense of by extracting features and patterns on its ownSemisupervised learning takes a middle ground It uses a smallamount of labeled data bolstering a larger set of unlabeled dataAnd reinforcement learning trains an algorithm with a rewardsystem providing feedback when an artificial intelligence agentperforms the best action in a particular situationLets walk through the kinds of datasets and problems that lendthemselves to each kind of learningWhat Is Supervised LearningIf youre learning a task under supervision someone is presentjudging whether youre getting the right answer Similarly insupervised learning that means having a full set of labeled datawhile training an algorithmFully labeled means that each example in the training dataset istagged with the answer the algorithm should come up with on itsown So a labeled dataset of flower images would tell the modelwhich photos were of roses daisies and daffodils When shown a newimage the model compares it to the training See Answer

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