LEADER 03945nam 2200481 450 001 9910467018403321 005 20200520144314.0 010 $a1-78847-155-5 035 $a(CKB)4100000005248905 035 $a(MiAaPQ)EBC5455319 035 $a(CaSebORM)9781788474221 035 $a(PPN)230108334 035 $a(Au-PeEL)EBL5455319 035 $a(OCoLC)1046616797 035 $a(EXLCZ)994100000005248905 100 $a20180816d2018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApache spark deep learning cookbook $eover 80 recipes that streamline deep learning in a distributed environment with Apache Spark /$fAhmed Sherif, Amrith Ravindra 205 $a1st edition 210 1$aBirmingham ;$aMumbai :$cPackt,$d2018. 215 $a1 online resource (462 pages) 311 $a1-78847-422-8 320 $aIncludes bibliographical references. 330 $aA solution-based guide to put your deep learning models into production with the power of Apache Spark Key Features Discover practical recipes for distributed deep learning with Apache Spark Learn to use libraries such as Keras and TensorFlow Solve problems in order to train your deep learning models on Apache Spark Book Description With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed. With the help of the Apache Spark Deep Learning Cookbook, you'll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you'll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you'll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras. By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark. What you will learn Set up a fully functional Spark environment Understand practical machine learning and deep learning concepts Apply built-in machine learning libraries within Spark Explore libraries that are compatible with TensorFlow and Keras Explore NLP models such as Word2vec and TF-IDF on Spark Organize dataframes for deep learning evaluation Apply testing and training modeling to ensure accuracy Access readily available code that may be reusable Who this book is for If you're looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python ... 606 $aData mining$xComputer programs 606 $aBig data 608 $aElectronic books. 615 0$aData mining$xComputer programs. 615 0$aBig data. 676 $a006.312 700 $aSherif$b Ahmed$0897913 702 $aRavindra$b Amrith 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910467018403321 996 $aApache spark deep learning cookbook$92006090 997 $aUNINA LEADER 00880nam0 22002411i 450 001 UON00195835 005 20231205103233.649 100 $a20030730d1949 |0itac50 ba 101 $arus 102 $aSU 105 $a|||| ||||| 200 1 $aVseob??aja istorija iskusstv. Tom 2. / M. V. Alpatov 210 $aMoskva$cIskusstvo$d1949 215 $a409 p.$ctav.$d26 cm. 606 $aArte$xStoria$3UONC022806$2FI 620 $aRU$dMoskva$3UONL003152 700 1$aALPATOV$bMichail V.$3UONV115389$0209197 801 $aIT$bSOL$c20250530$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00195835 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI AR DUOMO I 0010 $eSI EO 1852 5 0010 996 $aVseob??aja istorija iskusstv. Tom 2.$94297407 997 $aUNIOR