Deep Learning

Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of black art that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.

Our first non-review paper of the semester will be on using Deep RNNs to perform speech recognition tasks. This approach seeks to combine the advantages of deep neural networks wtih the "flexible use of long-range context that empowers RNNs". The abstract is rather lengthy, so I'll refrain from copying it here. Our weekly meeting on this paper will go over questions from the paper, strategies for reading more complex research papers, and how to identify strengths and weaknesses of journal articles.