且隨著學科的發(fā)展它們重合度也在增加。但它們之間的差異在哪兒NN機器學習和人工神經(jīng)網(wǎng)絡幾點關系：tsthedifferencebetweenMLandNN現(xiàn)今階段你所能看到的人工神經(jīng)網(wǎng)絡大部分是一種使用大量數(shù)據(jù)訓P對于神經(jīng)網(wǎng)絡算法的提高也使得它與機器學習方法有了很多共同之些方法的共同之處都是通過一些訓練數(shù)據(jù)及來尋找到一些滿足某些近日，一篇來自于斯坦福大學的人工智能定義短文ArtificialIntelligenceDefinitions從某一角度較為詳細的把智能相關的概Intelligencemightbedefinedastheabilitytolearnandconsistentbutnotintelligent.yArtificialIntelligence(AI),atermcoinedbyemeritusscanlearn,atleastsomewhatlikehumanbeingsdo.自主系統(tǒng)無需借助底層(微小)管理便可以自主對完成特定目標的Ahospitaldeliveryrobotmustautonomouslynavigatebusythesenseofbeingself-governingcommoninpoliticsorbiology.機器學習是人工智能中研究如何通過計算機軟件，在已有的經(jīng)驗和ragentscanimprovetheirperception,knowledge,thinking,orcomputerscience,statistics,psychology,neuroscience,economicsandcontroltheory.監(jiān)督學習計算機軟件從人類給定數(shù)據(jù)的標簽中來進行預測，比如從Insupervisedlearning,acomputerlearnstopredicthuman-givenlabels,suchasdogbreedbasedonlabeleddogpictures;unsupervisedlearningdoesnotrequirelabels,sometimesmakingitsownpredictiontaskssuchastryingtopredicteachsuccessivewordinasentence;reinforcementofgoodtechniques,enablingautonomy.深度學習則通過使用大型多層人工神經(jīng)網(wǎng)絡形成類似于人腦中神DeepLearningistheuseoflargemulti-layer(artificial)neuralnetworksthatcomputewithcontinuous(realnumber)representations,alittlelikethehierarchicallyorganizedMLapproach,usableforalltypesofML,withbetterandcomputebudgets.，butoftenjustforafewpartslikealearningorrewardfromdataorexperience,aseachangeinsystemdesignthatStanfordalumnusAndrejKarpathydubbedSoftware2.0.狹義的人工智能是為了完成特定任務，比如語音、人臉識別。類人智能，或者廣義智能則是探索更加一般性的智能，適用不同語境的。speechorfacialrecognition.Human-levelAI,orArtificialGeneralIntelligence(AGI),seeksbroadlysocialchatbotsorhuman-robotinteraction.聚焦人類人工智能則是為增加人的能力、解決社會需求