Md. Rezaul Karim - Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala (English Edition)

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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 L ead 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.

Dave Wentzel is the chief technology officer of Capax Global, a premier Microsoft consulting partner. Dave is responsible for setting the strategy and defining service offerings and capabilities for the data platform and Azure practice at Capax. He also works directly with clients to help them with their big data journeys. He is a frequent blogger and speaker on big data and data science topics.

Sumit Pal is a published author with Apress. He has more than 22 years of experience in software from startups to enterprises and is an independent consultant working with big data, data visualization, and data science. He builds end-to-end data-driven analytic systems.

Sumit has worked for Microsoft (SQLServer), Oracle (OLAP Kernel), and Verizon. He advises clients on their data architectures and build solutions in Spark and Scala. He has spoken at multiple conferences in North America and Europe and has developed a big data analyst training for Experfy. He has MS and BS in computer science.

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Machine learning has made a huge impact on academia and industry by turning data into actionable intelligence. Scala, on the other hand, has been observing a steady rise in its adoption over the last few years, especially in the field of data science and analytics. This book has been written for data scientists, data engineers, and deep learning enthusiasts who have a solid background with complex numerical computing and want to learn more hands-on machine learning application development.

So, if you're well-versed in machine learning concepts and want to expand your knowledge by delving into practical implementations 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, Zeppelin, DeepLearning4j, and MXNet.

After reading this book and practicing all of the projects, you will be able to dominate numerical computing, deep learning, and functional programming to carry out complex numerical tasks. You can thus develop, build, and deploy research and commercial projects in a production-ready environment.

This book isnt meant to be read cover to cover. You can turn the pages to a chapter that looks like something youre trying to accomplish or that simply ignites your interest. But any kind of improvement feedback is welcome.

Happy reading!

If you want to leverage the power of both Scala and open source libraries such as Spark ML, Deeplearning4j, H2O, MXNet, and Zeppelin to make sense of Big Data, then this book is for you. A strong understanding of Scala and the Scala Play Framework is recommended. Basic familiarity with ML techniques will be an added advantage.

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