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TensorFlow 2 Pocket Primer ['Campesato, Oswald']

Par : Contributeur(s) : Type de matériel : TexteTexteÉditeur : Mercury Learning and Information 2019Description : pType de contenu :
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  • 9781683924616
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Ressources en ligne : Abrégé : As part of thebest-selling Pocket Primer series, thisbook is designed to introducebeginners to basic machine learning algorithms using TensorFlow 2. It isintended to be a fast-paced introduction to various “core” features ofTensorFlow, with code samples that cover machine learning and TensorFlowbasics. A comprehensive appendix contains someKeras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. The material inthe chapters illustrates how to solve a variety of tasks after which you can dofurther reading to deepen your knowledge. Companion files with all of the codesamples are available for downloading from the publisher by emailing proof of purchase to info@merclearning.com.Features:Uses Python for codesamplesCovers TensorFlow 2 APIsand DatasetsIncludes a comprehensiveappendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMsFeatures the companion files with all of thesource code examples and figures (download fromthe publisher)
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As part of thebest-selling Pocket Primer series, thisbook is designed to introducebeginners to basic machine learning algorithms using TensorFlow 2. It isintended to be a fast-paced introduction to various “core” features ofTensorFlow, with code samples that cover machine learning and TensorFlowbasics. A comprehensive appendix contains someKeras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. The material inthe chapters illustrates how to solve a variety of tasks after which you can dofurther reading to deepen your knowledge. Companion files with all of the codesamples are available for downloading from the publisher by emailing proof of purchase to info@merclearning.com.Features:Uses Python for codesamplesCovers TensorFlow 2 APIsand DatasetsIncludes a comprehensiveappendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMsFeatures the companion files with all of thesource code examples and figures (download fromthe publisher)

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