LEADER 03941nam 2200481 450 001 9910827490303321 005 20200520144314.0 035 $a(CKB)4110000000007606 035 $a(CaSebORM)9781788479042 035 $a(MiAaPQ)EBC5259456 035 $a(Au-PeEL)EBL5259456 035 $a(CaPaEBR)ebr11509020 035 $a(OCoLC)1022793255 035 $a(EXLCZ)994110000000007606 100 $a20180306h20182018 uy 0 101 0 $aeng 135 $aurcn| ||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aScala machine learning projects $ebuild real-world machine learning and deep learning projects with Scala /$fMd. Rezaul Karim 205 $a1st edition 210 1$aBirmingham, England ;$aMumbai, [India] :$cPackt,$d2018. 210 4$d2018 215 $a1 online resource (470 pages) 300 $aIncludes index. 311 $a1-78847-904-1 330 $aPowerful 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,... 606 $aScala (Computer program language) 606 $aMachine learning 606 $aElectronic data processing 615 0$aScala (Computer program language) 615 0$aMachine learning. 615 0$aElectronic data processing. 676 $a005.114 700 $aKarim$b Md. Rezaul$0782103 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910827490303321 996 $aScala machine learning projects$94117394 997 $aUNINA