What is Deep Learning?
The most frequent question asked by my students is: Do I need to learn deep learning? Beyond the buzzwords bounced back and forth in blog posts and news articles, deep learning is probably the most revolutionary technology of the last century. Discovered in the 1950s and 60s, and further developed in the 1990s and early 2000s, deep learning remained mostly an academic topic until 2012, when a team led by Geoffrey Hinton won the Imagenet Challenge using an algorithm based on deep learning.
Although 2012 seems relatively recent, interest in deep learning exploded since then, as you can see in the chart below from Google Trends.
Deep Learning Explained
Figure from www.zerotodeeplearning.com
Deep learning is a branch of machine learning that has proven to be formidable in multiple domains and applications ranging from computer vision, natural language processing, speech synthesis, product recommendation, and robotic automation.
Deep learning is based on a technology called an artificial neural network, which is a very configurable mathematical function that can learn complex mappings between pairs of inputs and outputs. For example, a neural network can be trained to recognize objects in images by feeding it with a lot of data pairs (e.g., image and corresponding label). Similarly, it can be trained to translate English to Chinese by feeding it with lots of English sentences as input and the corresponding Chinese sentences as output.
If deep learning is just another machine learning technique, why has it become so popular and in high demand?
The main reason for the success of deep learning is that it works incredibly well with unstructured data, such as images, text, sound, time-series of events and so on. Traditional machine learning is capable of finding relations between pairs of input and output data represented as numbers. For example, a bank that is developing an