ds401深度學(xué)習(xí)tensorflow課程大綱

近年來，深度學(xué)各領(lǐng)域的應(yīng)用上突飛猛進(jìn)，也受到大家廣泛的矚目，但如何從零開部分:(1)介紹深度學(xué)習(xí)中的各種要素，以及(2)動(dòng)手建立你的第一個(gè)深度學(xué)習(xí)模WhatisdeeplearningandwhataresuccessfulWhyisdeeplearningdifferentfrommachinelearningandtraditionalstatistics?Whataretheessentialconceptsandskillsthata“deeplearner”shouldpossess?Whythisclasswillhelpyousucceedinyourtechnicalcareer(jobhuntingorstartingupyour ,etc)?FinallyweintroducetheagendaofDS401:classcontentsandprojectFeedforwardNeuralNetworkFeedforwardNeuralNetwork所需要的概念及技術(shù)簡(jiǎn)介下兩堂課內(nèi)容:FNN,CNN,RNN具體理論課程安排FunctionalityofdeepImportantapplicationsofdeepDatasetrepresentationandBackwardTraining,testingandcrossFeedforwardNeuralNetworkNetworkActivationLoss項(xiàng)目課程安排TensorFlow+KerasonFeedforwardNeuralNetwork(trainFeedforwardNeuralNetworkembeddingspacewordConvolutionalNeuralNetwork(trainFeedforwardNeuralNetworkStochasticgradientembeddingspacewordUnderstandingembeddingspacebyusingwordembeddingasApplicationsfromwordVariantstotrainawordConvolutionalNeuralNetworkWhyCNNforConvolutionallayerZeroChannels:greyscalev.s.colorfulMax示范對(duì)資料及模型的操作：inTensorFlowKerasGetfamiliarwithMNISTPreprocessFNNinTensorFlowKerasDesignnetworkstructure:towerv.s.Changeactivationfunction:isReLUtheSelectlossDropout:when,whereandhowL1andL2Regularization:whatarethefeedforwardneuralnetworkwordGetfamiliarwithtextrepresentationandnewsDesignandtrainaExaminethewordConvolutionalNeuralNetwork所需要的概念及技能講解深度學(xué)習(xí)進(jìn)階模型：RecurrentNeuralNetwork(RNN)RNNConvolutionalNeuralNetworkSelectionofthenumberandsizeofVariants:InceptionVariants:ResVariants:DEVISEforcotrainingimagesandRecurrentNeuralNetworkNetworkwithGradientdiminishandGatedRecurrentUnitLongShortTermMemoryCNNinTensorFlowKerasRevisitMNISTDesignconvolutio