Book Image

Machine Learning with Scala Quick Start Guide

By : Md. Rezaul Karim, Ajay Kumar N
Book Image

Machine Learning with Scala Quick Start Guide

By: Md. Rezaul Karim, Ajay Kumar N

Overview of this book

Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.
Table of Contents (9 chapters)

Neural network architectures

We can categorize DL architectures into four groups:

  • Deep neural networks (DNNs)
  • Convolutional neural networks (CNNs)
  • Recurrent neural networks (RNNs)
  • Emergent architectures (EAs)

However, DNNs, CNNs, and RNNs have many improved variants. Although most of the variants are proposed or developed for solving domain-specific research problems, the basic working principles still follow the original DNN, CNN, and RNN architectures. The following subsections will give you a brief introduction to these architectures.


DNNs are neural networks that have a complex and deeper architecture with a large number of neurons in each layer, and many connections between them. Although DNN refers to a very deep...