Simple ML methods that were used in small-scale data analysis are not effective anymore because the effectiveness of ML methods diminishes with large and high-dimensional datasets. Here comes DL—a branch of ML based on a set of algorithms that attempt to model high-level abstractions in data. Ian Goodfellow et al. (Deep Learning, MIT Press, 2016) defined DL as follows:
"Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones."
Similar to the ML model, a DL model also takes in an input, X, and learns high-level abstractions or patterns from it to predict an output of Y. For example, based on the stock prices...
