Book Image

Advanced Machine Learning with R

By : Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
Book Image

Advanced Machine Learning with R

By: Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
Table of Contents (30 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Summary


In this chapter, we learned about computer vision and its association with deep learning. We explored a specific type of deep learning algorithm, CNNs, that is widely used in computer vision. We studied an open source deep learning framework called MXNet. After a detailed discussion of the MNIST dataset, we built models using various network architectures and successfully classified the handwritten digits in the MNIST dataset. At the end of the chapter, we delved into the concept of transfer learning and explored its association with computer vision. The last project we built in this chapter classified images using an Inception-BatchNorm pretrained model. 

In the next chapter, we will explore an unsupervised learning algorithm called the autoencoder neural network. I am really excited to implement a project to capture credit card fraud using autoencoders. Are you game? Let's go!