Tutorial on deep learning and applications in non-cognitive domains

In Dec 2016, I delivered a tutorial on deep learning and its applications in non-cognitive domains at AusDM'16. It covers:

• Basic concepts of deep learning: best practices in machine learning, three architectures (feedforward, recurrent and convolutional nets), enablers of deep learning (adaptive stochastic gradient, skip-connections, gating, dropouts).
• Current practices in data mining: programming frameworks, tricks of the trade, case-studies in non-cognitive domains (healthcare, software engineering & anomaly detection). 
• Advanced topics for research: deep learning in relational domains, unsupervised learning, memory & attention, how to position yourself in this highly dynamic game.

The materials are accessible here.

A generic code for sequential conditional random fields

I have made available the C++ code for sequential labelling tasks such as part-of-speech tagging, noun-phrase chunking or human activity annotation. The code is domain independent and supports the following features:

• First & second-order sequential CRFs.
• Training algorithms: L-BFGS (from Taku Kudo), stochastic gradient ascent and Collin's voted perceptron.
• Handle missing training labels.
• Decoding: Viterbi (forward/backward) and Pearl's max-product.
• Report label-wise and segment-wise statistics.

The project page is here. The page contains a tutorial on the theory of CRFs and a discussion on the implementation.