000			03724cam a2200277zu 4500
001			88856701
003			FRCYB88856701
005			20250107230151.0
006			m o d
007			cr un
008			250108s2018 fr | o|||||0|0|||eng d
020			_a9781788479042
035			_aFRCYB88856701
040			_aFR-PaCSA _ben _c _erda
100	1		_aKarim, Md. Rezaul
245	0	1	_aScala Machine Learning Projects _c['Karim, Md. Rezaul']
264		1	_bPackt Publishing _c2018
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aKarim, Md. Rezaul
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88856701 _qtext/html _a
520			_aPowerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.About This BookExplore 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 wayCover 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 ForIf 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 LearnApply advanced regression techniques to boost the performance of predictive modelsUse different classification algorithms for business analytics Generate trading strategies for Bitcoin and stock trading using ensemble techniquesTrain Deep Neural Networks (DNN) using H2O and Spark MLUtilize NLP to build scalable machine learning models Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML applicationLearn how to use autoencoders to develop a fraud detection applicationImplement LSTM and CNN models using DeepLearning4j and MXNetIn DetailMachine 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, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.Style and approachLeverage the power of machine learning and deep learning in different domains, giving best practices and tips from a real world case studies and help you to avoid pitfalls and fallacies towards decision making based on predictive analytics with ML models.
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