Deep learning has transformed many traditional businesses, such as web search, advertising, and many more. A major challenge with the traditional machine learning approaches is that we need to spend a considerable amount of time choosing the most appropriate feature selection process before modeling. Besides this, these traditional techniques operate with some level of human intervention and guidance. However, with deep learning algorithms, we can get rid of the overhead of explicit feature selection since it is taken care of by the models themselves. These deep learning algorithms are capable of modeling complex and non-linear relationships within the data. In this book, we'll introduce you to how to set up a deep learning ecosystem in R. Deep neural networks use sophisticated mathematical modeling techniques to process data in complex ways. In this book, we'll showcase the use of various deep learning libraries, such as keras and MXNet, so that you can utilize their enriched set of functions and capabilities in order to build and execute deep learning models, although we'll primarily focus on working with the keras library. These libraries come with CPU and GPU support and are user-friendly so that you can prototype deep learning models quickly.
In this chapter, we will demonstrate how to set up a deep learning environment in R. You will also get familiar with various TensorFlow APIs and how to implement a neural network using them. You will also learn how to tune the various parameters of a neural network and also gain an understanding of various activation functions and their usage for different types of problem statements.
In this chapter, we will cover the following recipes:
- Setting up the environment
- Implementing neural networks with Keras
- TensorFlow Estimator API
- TensorFlow Core API
- Implementing a single-layer neural network
- Training your first deep neural network