Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala

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Packt Publishing Ltd, 31.01.2018 - 470 Seiten
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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 Description

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, 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 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.

 

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Inhalt

Preface
1
Analyzing Insurance Severity Claims
8
Analyzing and Predicting Telecommunication Churn
57
High Frequency Bitcoin Price Prediction from Historical and Live Data
102
PopulationScale Clustering and Ethnicity Prediction
142
Topic Modeling A Better Insight into LargeScale Texts
182
Developing Modelbased Movie Recommendation Engines
206
Options Trading Using Qlearning and Scala Play Framework
233
Subscription Assessment for Bank Telemarketing using Deep Neural Networks
282
Fraud Analytics Using Autoencoders and Anomaly Detection
320
Human Activity Recognition using Recurrent Neural Networks
360
Image Classification using Convolutional Neural Networks
394
Other Books You May Enjoy
438
Index
441
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Über den Autor (2018)

Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a Researcher at the Insight Centre for Data Analytics, Ireland. Before that, he worked as a Lead Engineer at Samsung Electronics, Korea. He has 9 years of R&D experience with C++, Java, R, Scala, and Python. He has published several research papers concerning bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, DeepLearning4j, MXNet, and H2O.

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