Book Image

R Machine Learning Projects

By : Dr. Sunil Kumar Chinnamgari
Book Image

R Machine Learning Projects

By: Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
Table of Contents (12 chapters)
10
The Road Ahead

Implementing computer vision with pretrained models

In Chapter 1, Exploring the Machine Learning Landscape, we touched upon a concept called transfer learning. The idea is to take the knowledge learned in a model and apply it to another related task. Transfer learning is used on almost all computer vision tasks nowadays. It's rare to train models from scratch unless there is a huge labeled dataset available for training.

Generally, in computer vision, CNNs try to detect edges in the earlier layers, shapes in the middle layer, and some task-specific features in the later layers. Irrespective of the image to be detected by the CNNs, the function of the earlier and middle layers remains the same, which makes it possible to exploit the knowledge gained by a pretrained model. With transfer learning, we can reuse the early and middle layers and only retrain the later layers. It...