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)

NNs with Keras

Knowing, at least intuitively, the components of a deep learning model and how they interact is a must before going any further into practical details. The practical details might change with respect to which deep learning framework and API to be used; this chapter uses Keras. It will give access to Google's TensorFlow and some other frameworks.

Keras is the ancient Greek word for horn, which makes reference to Odyssey, written by Homer. In his narrative, the spirits that came from a gate made of polished horn had fulfilling visions and accurate predictions.

Building cutting-edge models using Keras is easy. The workflow usually goes as follows: once you are done with data preprocessing, you design the network's architecture and choose a learning strategy, a cost function, and measures to track. The next steps are training and testing.

The process of...