Yesterday I attended the “Andrew Ng and pyStruct” meetup.
I was lucky enough to get a place to the meetup due to the Germany-Portugal game that happened on the same time 🙂
The first part by Andrew Ng was a video meetup joint to 3 locations – Paris, Zurich and Berlin. Andrew Ng is a co-founder of Coursera and a Machine Learning guru. He teaches the ML course in Coursera which is one of the most popular courses in Coursera (took it myself and it is a very good and structured introduction to machine learning, new session started yesterday). He teaches in Standford and soon he will be leaving to Baidu research.
The talk included 15-20 minutes of introduction to deep learning, recent results, applications and challenges. He mainly focused on scaling up deep learning algorithms for using billions features \ properties. The rest of the talk was question answering mostly regarding the theoretical aspects of deep learning, future challenges, etc. For me one of the most important things he said was “innovation is a result of team work”.
Some known applications of deep learning is – speak recognition, image processing, etc.
In the end he suggested taking Stanford deep learning tutorial – http://deeplearning.stanford.edu/tutorial/
T.R – there are currently 2 python packages I know which deal with deep learning –
The next talk was given by Andreas Mueller. You can find his slides here.
Muelller introduced structured prediction which is a natural extension or a generalization of regression problems to a structured output rather than just a number \ class . Structured learning has advantage over other algorithms of supervised learning as it can learn several properties at once and use the correlations between those properties.
Example – costumers data, several properties of costumers – gender, marriage status, has children, owns a car, etc. One can guess that married and has children properties are highly correlated and that when learning about those properties together there is a better chance of getting good results. It is better than LDA in the sense that it has less classes (not every combination of the variables is a class) and it requires less training data.
Other examples include pixel classification – classifying each pixel to an object in the image and OCR, etc.
He then talked about PyStruct – a python package for structured prediction. Actually not much to add that is not written in the documents.