02779nam 2200601 450 991046066300332120200520144314.01-78238-586-X(CKB)3710000000355267(EBL)1707820(OCoLC)903489480(SSID)ssj0001423752(PQKBManifestationID)12618404(PQKBTitleCode)TC0001423752(PQKBWorkID)11440563(PQKB)10482267(MiAaPQ)EBC1707820(Au-PeEL)EBL1707820(CaPaEBR)ebr11019572(CaONFJC)MIL728739(EXLCZ)99371000000035526720150216h20152015 uy 0engur|n|---|||||txtrdacontentcrdamediacrrdacarrierThinking through sociality an anthropological interrogation of key concepts /edited by Vered AmitNew York, New York ;Oxford, England :berghahn,2015.©20151 online resource (216 pages)Description based upon print version of record.1-322-97457-8 1-78238-585-1 Includes bibliographical references at the end of each chapters and index.""Thinking through Sociality""; ""Thinking through Sociality - An Anthropological Interrogation of Key Concepts - Edited by Vered Amit""; ""Contents""; ""Acknowledgements""; ""Introduction - Thinking through Sociality""; ""1 - Disjuncture""; ""2 - Fields""; ""3 - Social Space""; ""4 - Sociability""; ""5 - Organizations""; ""6 - Network""; ""Epilogue - Sociality and Uncertainty""; ""Notes on Contributors""; ""Index""As issues and circumstances investigated by anthropologists are becoming ever more diverse, the need to address social affiliation in contemporary situations of mobility, urbanity, transnational connections, individuation, media, and capital flows, has never been greater. Thinking Through Sociality combines a review of classical theories with recent theoretical innovations across a wide range of issues, locales, situations and domains. In this book, an international group of contributors train attention on the concepts of disjuncture, field, social space, sociability, organizations and networEthnologySocial interactionAnthropologyElectronic books.Ethnology.Social interaction.Anthropology.302Amit VeredMiAaPQMiAaPQMiAaPQBOOK9910460663003321Thinking through sociality2094803UNINA03941nam 2200481 450 991082749030332120200520144314.0(CKB)4110000000007606(CaSebORM)9781788479042(MiAaPQ)EBC5259456(Au-PeEL)EBL5259456(CaPaEBR)ebr11509020(OCoLC)1022793255(EXLCZ)99411000000000760620180306h20182018 uy 0engurcn| |||||txtrdacontentcrdamediacrrdacarrierScala machine learning projects build real-world machine learning and deep learning projects with Scala /Md. Rezaul Karim1st editionBirmingham, England ;Mumbai, [India] :Packt,2018.20181 online resource (470 pages)Includes index.1-78847-904-1 Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming. About This Book Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework. Who This Book Is For If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful. What You Will Learn Apply advanced regression techniques to boost the performance of predictive models Use different classification algorithms for business analytics Generate trading strategies for Bitcoin and stock trading using ensemble techniques Train Deep Neural Networks (DNN) using H2O and Spark ML Utilize NLP to build scalable machine learning models Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application Learn how to use autoencoders to develop a fraud detection application Implement LSTM and CNN models using DeepLearning4j and MXNet In Detail Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end,...Scala (Computer program language)Machine learningElectronic data processingScala (Computer program language)Machine learning.Electronic data processing.005.114Karim Md. Rezaul782103MiAaPQMiAaPQMiAaPQBOOK9910827490303321Scala machine learning projects4117394UNINA