Book Image

Scala Machine Learning Projects

Book Image

Scala Machine Learning Projects

Overview of this book

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 (17 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Summary


In this chapter, we have seen how to use and build real-life applications using CNNs, which are a type of feedforward artificial neural network in which the connectivity pattern between neurons is inspired by the organization of the animal visual cortex. Our image classifier application using CNN can classify real-life images with an acceptable level of accuracy, although we did not achieve higher accuracy. However, readers are encouraged to tune hyperparameters in the code and also try the same approach with another dataset.

Nevertheless, and importantly since the internal data representation of a convolutional neural network does not take into account important spatial hierarchies between simple and complex objects, CNN has some serious drawbacks and limitation for certain instances. Therefore, I would suggest you take a look at the recent activities around capsule networks on GitHub at https://github.com/topics/capsule-network. Hopefully, you can get something useful out from there...