Deep Learning – A Term To Know

Deep Learning is a new term that is starting to appear in the data science/machine learning news.

• Communications of the ACM just published a story on the topic, Deep Learning Comes of Age.
• Deep Learning was named as one of the Top 10 Breakthrough Technologies of 2013 by MIT Technology Review.
• Jeremy Howard, Chief Scientist at Kaggle declared Deep Learning – The Biggest Data Science Breakthrough of the Decade.
• The New York Times published Scientists See Promise in Deep-Learning Programs

What is Deep Learning?

According to DeepLearning.net, the definition goes like this:

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

Wikipedia provides the following defintion:

Deep learning is set of algorithms in machine learning that attempt to learn layered models of inputs, commonly neural networks. The layers in such models correspond to distinct levels of concepts, where higher-level concepts are defined from lower-level ones, and the same lower-level concepts can help to define many higher-level concepts.

Deep Learning is sometimes referred to as deep neural networks since much of deep learning focuses on artificial neural networks. Artificial neural networks are a technique in computer science modelled after the connections (synapses) of neurons in the brain. Artificial neural networks, sometimes just called neural nets, have been around for about 50 years, but advances in computer processing power and storage are finally allowing neural nets to improve solutions for complex problems such as speech recognition, computer vision, and Natural Language Processing (NLP).

Hopefully, this blog post provides some inspiration and useful links to help you learn more about deep learning.

How is Deep Learning being applied?

The following talk, Tera-scale Deep Learning, by Quoc V. Le of Stanford gives some indication of the size of problems to be tackled. The talk discusses work being done on a cluster of 2000 machines and more than 1,000,000,000 parameters.