All deep learning is machine learning, but not all machine learning is deep learning. Throughout this book, we will focus on processes and techniques that are specific to deep learning in R. However, all the core principles of machine learning are essential to understand before we can move on to explore deep learning.

Deep learning is marked as a special subset of machine learning based on the use of neural networks that mimic brain activity behavior. The learning is referred to as being deep because, during the modeling process, the data is manipulated by a number of hidden layers. In this type of modeling, specific information is gathered from each layer. For example, one layer may find the edges of images while another finds particular hues.

Notable applications for this type of machine learning include the following:

• Image recognition (including facial recognition)
• Signal detection
• Recommendation systems
• Document summarization
• Topic modeling
• Forecasting
• Solving games
• Moving an object through space, for example, self-driving cars

All of these topics will be covered throughout the course of this book. All of these topics implement deep learning and neural networks, which are primarily used for classification and regression.