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Hands-On Data Science with R

You're reading from  Hands-On Data Science with R

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Pages 420 pages
Edition 1st Edition
Languages
Authors (4):
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Profile icon Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Profile icon Doug Ortiz
Nataraj Dasgupta Nataraj Dasgupta
Profile icon Nataraj Dasgupta
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Profile icon Ricardo Anjoleto Farias
View More author details
Toc

Table of Contents (16) Chapters close

Preface 1. Getting Started with Data Science and R 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

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...
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