Throughaseriesofrecentbreakthroughs,deeplearninghasboostedtheentirefieldofmachinelearning.Now,evenprogrammerswhoknowclosetonothingaboutthistechnologycanusesimple,efficienttoolstoimplementprogramscapableoflearningfromdata.Thispracticalbookshowsyouhow.Byusingconcreteexamples,minimaltheory,andtwoproduction-readyPythonframeworks—Scikit-LearnandTensorFlow—authorAurélienGéronhelpsyougainanintuitiveunderstandingoftheconceptsandtoolsforbuildingintelligentsystems.You’lllearnarangeoftechniques,startingwithsimplelinearregressionandprogressingtodeepneuralnetworks.Withexercisesineachchaptertohelpyouapplywhatyou’velearned,allyouneedisprogrammingexperiencetogetstarted.*Explorethemachinelearninglandscape,particularlyneuralnets*UseScikit-Learntotrackanexamplemachine-learningprojectend-to-end*Exploreseveraltrainingmodels,includingsupportvectormachines,decisiontrees,randomforests,andensemblemethods*UsetheTensorFlowlibrarytobuildandtrainneuralnets*Diveintoneuralnetarchitectures,includingconvolutionalnets,recurrentnets,anddeepreinforcementlearning*Learntechniquesfortrainingandscalingdeepneuralnets
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