eBook: Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala (DRM PDF)
 
電子書格式: DRM PDF
作者: Karim Md. Rezaul Karim 
書城編號: 21880173


售價: $377.00

購買後立即進貨, 約需 1-4 天

 
 
製造商: Packt Publishing
出版日期: 2018/01/31
頁數: 470
ISBN: 9781788471473

商品簡介
Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.Key FeaturesExplore 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. Book DescriptionMachine 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.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 MXNetWho 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.
Karim Md. Rezaul Karim 作者作品表

eBook: Machine Learning with Scala Quick Start Guide: Leverage popular machine learning algorithms and techniques and implement them in Scala (DRM PDF

eBook: Machine Learning with Scala Quick Start Guide: Leverage popular machine learning algorithms and techniques and implement them in Scala (DRM EPU

eBook: Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs (DRM PDF)

eBook: Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs (DRM EPUB)

eBook: TensorFlow: Powerful Predictive Analytics with TensorFlow: Predict valuable insights of your data with TensorFlow (DRM PDF)

eBook: TensorFlow: Powerful Predictive Analytics with TensorFlow: Predict valuable insights of your data with TensorFlow (DRM EPUB)

eBook: Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala (DRM EPUB)

eBook: Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala (DRM PDF)

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