Book Image

Hands-On Data Science with R

By : Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias
Book Image

Hands-On Data Science with R

By: Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias

Overview of this book

R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.
Table of Contents (16 chapters)

Overview – NNs and deep learning

This section is designed to introduce the core components of NNs and deep learning. For those who already are familiar with NNs, it may feel like a condensed overview of the topic, but feel free to jump to the next section if you are here only for the practical tips about Keras.

An NN, or ANN to avoid any confusion, is a powerful method that can approximate any sort of function, linear or not. If you don't know anything about ANNs, here you will get the basics: the main components, and how the training takes place. You might learn which are the hyperparameters and algorithms to choose while building a network. We will discuss matters such as the following ones:

  • How many nodes should I use in each layer?
  • Which activation functions should I use?
  • How is data transformation likely to alter the results?
  • Which error measure should I adopt...