“Seven Essential Machine Learning Equations”

Seven Essential Machine Learning Equations: A Cribsheet (Really, the Précis)

September 9, 2017 AJMaren Comments 17 comments

Making Machine Learning As Simple As Possible Albert Einstein is credited with saying, Everything should be made as simple as possible, but not simpler. Machine learning is not simple. In fact, once you get beyond the simple “building blocks” approach of stacking things higher and deeper (sometimes made all too easy with advanced deep learning packages), you are in the midst of some complex stuff. However, it does not need to be more complex than it has to be. …

A Tale of Two Probabilities

September 7, 2017 AJMaren Comments 7 comments

Probabilities: Statistical Mechanics and Bayesian: Machine learning fuses several different lines of thought, including statistical mechanics, Bayesian probability theory, and neural networks. There are two different ways of thinking about probability in machine learning; one comes from statistical mechanics, and the other from Bayesian logic. Both are important. They are also very different. While these two different ways of thinking about probability are usually very separate, they come together in some of the more advanced machine learning topics, such…

Seven Statistical Mechanics / Bayesian Equations That You Need to Know

August 2, 2017 AJMaren Comments 9 comments

Essential Statistical Mechanics for Deep Learning If you’re self-studying machine learning, and feel that statistical mechanics is suddenly showing up more than it used to, you’re not alone. Within the past couple of years, statistical mechanics (statistical thermodynamics) has become a more integral topic, along with the Kullback-Leibler divergence measure and several inference methods for machine learning, including the expectation maximization (EM) algorithm along with variational Bayes. Statistical mechanics has always played a strong role in machine…

Alianna J. Maren

Statistical Mechanics, Neural Networks, Artificial Intelligence

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