NBDSM

What

nBDSM is the noiseBridge Deepnet and Statistical Mechanics working group. We meet weekly to learn, teach, and discuss topics at the intersection of AI/deep learning and statistical mechanics. Note that we have a non-trivial overlap with The One, The Only Noisebridge DreamTeam.

We're focused on theory. Implementation is fun too, but has its own set of skills that are mostly orthogonal to what we'll be covering, so our focus on it will be light.

Prerequisites

Our discussions are usually at graduate level in machine learning and theoretical physics. To be able to get something out of them, you should have at least undergrad proficiency in

• linear algebra (at the level of D. Lay's book)
• single and multi-variable calculus, and vector calculus (all of Stewart)
• statistics, including bayesian
• statistical mechanics (at the level of McGreevy's MIT lecture notes)

Links

Here are some cool links (you can use these to figure out what to study to get up to speed)

• a great talk by Ganguli at last year's deep learning summer school in Montreal.
• anything recent by Ganguli at the Neural Dynamics and Computation Lab as well.
• Calculated Content
• the venerable colah's blog

Papers

Good large scale overview of why the stat mech side is important

• Advani et. al. - Stat mech of complex neural systems and high dimensional data - arXiv:1301.7115v1

More stuff

• Mastromatteo - On the typical properties of inverse problems in stat mech - arXiv:1311.0910v1