Deep Learning Summer School, Montreal 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. If that is you, there are plenty of videos to help you learn more.
A fun video to watch. Very Impressive!
The technique uses a genetic algorithm to training a neural network. A paper with more details can be found at, Evolving Neural Networks through Augmenting Topologies (NEAT)
Here are some great resources to kickstart your deep learning.
- Deep Learning in Nutshell – nicely detailed blog post
- Deep Learning Resources – so many good resources, more than I will list here, follow the link
- Deep Learning Tutorial – just like the title says
- Deep Neural Networks are Easily Fooled – Proof that deep learning cannot do everything
- Deep Learning in Neural Networks: An Overview – 88 page tutorial
Deep Learning is the hottest topic in all of data science right now. Adam Gibson, cofounder of Blix.io, has created an open source deep learning library for Java named DeepLearning4j. For those curious, DeepLearning4j is open sourced on github.
Below is a video of Adam introducing deep learning and DeepLearning4j. Also, if you are interested in learning more about deep learning. Here are a couple more very help links.
Recently, MIT Technology Review ran an article about the new uses of deep learning at Facebook. Facebook would like to use deep learning to understand more about its users. They have assembled quite a team.
If you are looking to learn more about deep learning, Andrew Ng, cofounder of Coursera, has some course materials on deep learning available on the Stanford Openclassroom site. The materials appear incomplete, but they do provide lectures covering neural networks which are the foundations of deep learning.
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.
This posts provides a nice quick overview of 6 machine learning algorithms.
- Decision Trees
- Linear Regression
- Neural Networks
- Bayesian Networks
- Support Vector Machines (SVMs)
- Nearest Neighbor