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Thoughts on Data Science, Machine Learning, and AI

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Free Energies and Variational Inference

My Labor Day Holiday Blog: for those on email, I will add updates, answer questions,  and make corrections over the…

What is Free Energy: Hinton, Helmholtz, and Legendre

Hinton introduced Free Energies in his 1994 paper, Autoencoders, minimum description length, and Helmholtz Free Energy This paper, along with…

Interview with a Data Scientist

It’s always fun to be interviewed. Check out my recent chat with Max Mautner, the Accidental Engineer http://theaccidentalengineer.com/charles-martin-principal-consultant-calculation-consulting/

Normalization in Deep Learning

A few days ago (Jun 2017), a 100 page on Self-Normalizing Networks appeared.  An amazing piece of theoretical work, it…

Why Deep Learning Works 3: BackProp minimizes the Free Energy ?

 ?Deep Learning is presented as Energy-Based Learning Indeed, we train a neural network by running BackProp, thereby minimizing the model error–which is…

Foundations: Mean Field Boltzmann Machines 1987

A friend from grad school pointed out a great foundational paper on Boltzmann Machines.  It is a 1987 paper from…

Improving RBMs with physical chemistry

Restricted Boltzmann Machines (RBMs) are like the H atom of Deep Learning. They are basically a solved problem, and while of…

MMDS 2016 Video Presentation

Why Deep Learning Works: Perspectives from Theoretical Chemistry:

on Cheap Learning: Partition Functions and RBMs

“Why does deep and cheap learning work so well?“ This is the question posed by a recent article.  Deep Learning…

Why Deep Learning Works III: a preview

A preview of my upcoming talk at mmds this week: CC mmds talk 2106 from Charles Martin Stay tuned for…

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