The use of deep learning has grown rapidly over the past decade, thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to Emergen Research, which expects deep learning to become a $93 billion market by 2028.
But what exactly is deep learning and how does it work?
Deep learning is a subset of machine learning which uses neural networks to perform learning and predictions. Deep learning has shown amazing performance in various tasks, whether it be text, time series or computer vision. The success of deep learning comes primarily from the availability of large data and compute power. However, it is more than that, which makes deep learning far better than any of the classical machine learning algorithms.
Deep learning: Neural networks and functions
A neural network is an interconnected network of neurons with each neuron being a limited function approximator. This way, neural networks are considered as universal function approximators. If you recall from high school math, a function is a mapping from input space to an output space. A simple sin(x) function is mapping from angular space (-180o to 180 o or 0 o to 360 o) to real number space (-1 to 1).