Stochastic Hopfield net Boltzmann machine is nothing but stochastic Hopfield net1. If you did not yet read the post of the Hopfield net in the blog, just go read it. I assume the readers are familiar to it, and directly use many results we had in the post. The magic of deep learning which we have discussed a couple of times works here, too. Such as $\epsilon$-greedy off-policy algorithm2, the stochastic character of the binary units allows the machine occasionally increase its energy to escape from poor local minima.

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Judgement Day It is the first time I did not post for 4 days. I was too busy to prepare for the meetup this week. The day before yesterday meetup topic was the reinforcement learning as I mentioned at previous post. It is not a long research paper, but includes 143 references. Ah, not my favorite. This A Brief Survey of Deep Reinforcement Learning did not explain the detail of what I am interested in.

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Hard K-means and responsibilities If you did not read the first part of the clustering series. Please go check it out. I use the same data points and this post starts from troubleshooting the hard K-means algorithm in the previous post. In the previous post, we defined assignment. The equivalent representation of this assignment of points to clusters is given by responsibilities, $r^{(n)}_k$. In the assignment step, we set $r^{(n)}_k$ to one if mean k is the closest mean to datapoint $ {\textbf x}^{(n)}$; otherwise, $r^{(n)}_k$ is zero.

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Namshik Kim

physicist, data scientist

Data Scientist

Vancouver, BC, Canada.