Book Image

R Deep Learning Projects

Book Image

R Deep Learning Projects

Overview of this book

R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.
Table of Contents (11 chapters)

Chapter 2. Traffic Sign Recognition for Intelligent Vehicles

Convolutional neural networks (CNNs) are so useful in computer vision that we are going to use one for another application, traffic sign detection for intelligent vehicles. We will also cover several important concepts of deep learning in this chapter and will get readers exposed to other popular frameworks and libraries for deep learning.

We continue our R deep learning journey with one of the core problems in self-driving cars, object recognition, and to be specific, traffic sign classification. To avoid accidents and ensure safety, robust traffic sign classification is critical to realizing driving autonomy. We will start with what self-driving cars are and what aspects deep learning is applied to. We will also discuss how deep learning stands out and becomes the state-of-the-art solution for object recognition in intelligent vehicles. With the background knowledge in mind, we'll get started with our project when we first conduct...