Scala Machine Learning Projects

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.

Scala Machine Learning Projects

Md. Rezaul Karim

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.
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Book Details

ISBN 139781788479042
Paperback470 pages

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.

Table of Contents

Chapter 8: Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks

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

Authors

Table of Contents

Chapter 8: Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks

Book Details

ISBN 139781788479042
Paperback470 pages
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