Theclevermachine.wordpress.com Website Review

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Theclevermachine.wordpress.com receives about 38913 visitors in one month. That could possibly earn $194.57 each month or $6.49 each day. Server of the website is located in the United States. Theclevermachine.wordpress.com main page was reached and loaded in 0.74 seconds. This is a good result. Try the services listed at the bottom of the page to search for available improvements.

Traffic and Value

Is theclevermachine.wordpress.com legit?
Website Value	$3503
Alexa Rank	547244
Monthly Visits	38913
Daily Visits	1298
Monthly Earnings	$194.57
Daily Earnings	$6.49

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Theclevermachine.wordpress.com Server Location

Country: United States
Metropolitan Area: San Francisco
Postal Reference Code: 94110
Latitude: 37.7506
Longitude: -122.4121

The United States is a good location for your server if your website will be receiving most or at least decent part of visitors from Northern America. If you are planning to target mostly other regions you should consider using CDN or changing your server location.

Summarized Content

In this post I will review the gradient descent algorithm that is commonly used to train the general clas* of models known as Boltzmann machines. Though the primary goal of the post is to supplement another post on restricted Boltzmann machines, I hope that those readers who are curious about how Boltzmann machines are trained, but have found it difficult to track down a complete or straight-forward derivation of the maximum likelihood learning algorithm for these models (as I have), will also find the post First, a little background: Boltzmann machines are stochastic neural networks that can be thought of as the probabilistic extension of the between Boltzmann machines and neural networks, the random variables are often are often referred to as “units.” The role of the visible units is to approximate the true distribution of the data, while the role of the latent variables it to extend the expressiveness of the model by capturing underlying features in the observed data. The latent variables are often referred to as hidden units, as they do not result directly from the observed data and are generally marginalized over to obtain the likelihood of and hidden units (but no connections from any unit to itself). The graphical model for the general Boltzmann machine is shown in Figure Given the current state of the visible and hidden units, the overall configuration of the model network is described by a connectivity that capture offsets for each of the hidden and visible units. The Boltzmann machine has been used for years in field of statistical mechanics to model physical systems based on the principle of energy minimization. In the statistical mechanics, the connectivity function is often referred to the “energy function,” a term that is has also been standardized in the statistical learning literature. Note that the energy function returns a single scalar value for any configuration of the network parameters and random variable states.

Theclevermachine Main Page Content

HTML Tag	Content	Informative?
Title:	The Clever Machine \| Topics in Computational Neuroscience & Machine
Description:	Topics in Computational Neuroscience & Machine	Could be improved
H1:	The Clever Machine	Is it informative enough?
H2:	Topics in Computational Neuroscience & Machine Learning
H3:	Some Background and Notation	Is it informative enough?

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Internal Pages

/about-me/:
Title	About the Author \| The Clever Machine
Description	Hi! My Name is Dustin Stansbury. I recently received my PhD from UC Berkeley in Vision Science under the supervision of Jack Gallant. My academic research interests involve, but are not limited to: Neural Computation Machine Learning (particularly neural networks) Natural Scene Statistics Visual Perception Computer Vision Signal Processing Neuroimaging (e.g. Functional Magnetic Resonance Imaging (fMRI)) My…
H1	The Clever Machine
H2	Topics in Computational Neuroscience & Machine Learning
H3	Share this:

/about-theclevermachine/:
Title	About The Clever Machine \| The Clever Machine
Description	This blog is essentially an attempt to distill some of the knowledge I've gained through my graduate studies in computational neuroscience. The content of the blog will be posts about ysis techniques, algorithms, theory, or things I think are cool, exciting, or helpful. I plan for most posts to be based around a source code implementation**… [censored]
H1	The Clever Machine
H2	Topics in Computational Neuroscience & Machine Learning
H3	Share this:

/interact/:
Title	Blog Interface \| The Clever Machine
Description	To optimize scientific density in the posts, all things involving the user interface have been relocated to this centralized location. The reader can use this page to navigate, add general comments, etc.
H1	The Clever Machine
H2	Topics in Computational Neuroscience & Machine Learning
H3	Share this:

/2014/09/23/derivation-maximum-likelihood-for-boltzmann-machines/:
Title	Derivation: Maximum Likelihood for Boltzmann Machines \| The Clever Machine
Description	In this post I will review the gradient descent algorithm that is commonly used to train the general cl of models known as Boltzmann machines. Though the primary goal of the post is to supplement another post on restricted Boltzmann machines, I hope that those readers who are curious about how Boltzmann machines are trained, but have found it difficult to track down a… [censored]
H1	The Clever Machine
H2	Topics in Computational Neuroscience & Machine Learning
H3	Share this:

/author/dustinstansbury/:
Title	dustinstansbury \| The Clever Machine
Description	Read all of the posts by dustinstansbury on The Clever Machine
H1	The Clever Machine
H2	Topics in Computational Neuroscience & Machine Learning
H3	Some Background and Notation

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