In this chapter, the goal was to get you up and running in the exciting world of neural networks and deep learning. We examined how the methods work, their benefits, and their inherent drawbacks with applications to two different datasets. These techniques work well where complex, nonlinear relationships exist in the data. However, they are highly complex, potentially require a ton of hyper-parameter tuning, are the quintessential black boxes, and are difficult to interpret. We don't know why the self-driving car made a right on red, we just know that it did so properly. I hope you will apply these methods by themselves or supplement other methods in an ensemble modeling fashion. Good luck and good hunting! We will now shift gears to unsupervised learning, starting with clustering.
Mastering Machine Learning with R, Second Edition - Second Edition
Mastering Machine Learning with R, Second Edition - Second Edition
Overview of this book
This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.
You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do.
With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Related Content you might be interested in
Current Title:
Mastering Machine Learning with R, Second Edition - Second Edition
Table of Contents (23 chapters)
Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
Free Chapter
A Process for Success
Linear Regression - The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks and Deep Learning
Cluster Analysis
Principal Components Analysis
Market Basket Analysis, Recommendation Engines, and Sequential Analysis
Creating Ensembles and Multiclass Classification
Time Series and Causality
Text Mining
R on the Cloud
R Fundamentals
Sources
Customer Reviews