CORRELATION


How Bayesian Networks Are Superior in Understanding Effects of Variables

@machinelearnbot

Bayes Nets (or Bayesian Networks) give remarkable results in determining the effects of many variables on an outcome. They typically perform strongly even in cases when other methods falter or fail. These networks have had relatively little use with business-related problems, although they have worked successfully for years in fields such as scientific research, public safety, aircraft guidance systems and national defense. Importantly, they often outperform regression, particularly in determining variables' effects. Regression is one of the most august multivariate methods, and among the most studied and applied.


Incumbents need machine learning to ward off fintech challengers

#artificialintelligence

New players are rapidly gaining higher market share by attracting customers with digital products and innovative services, many of which are made possible through machine learning and artificial intelligence. Such players can easily meet customer demand as they already have lean and agile digital operations. To maintain leadership, incumbents must keep pace with the new normal of rapid disruption. With its capacity to learn from large datasets and establish patterns and correlations, machine learning can revolutionize operations. It can inject new efficiencies into tasks such as risk assessment, fraud detection, anti-money-laundering, trading and customer service by providing instant insights, relevant recommendations and informed decisions in real time.


Incumbents need machine learning to ward off fintech challengers

#artificialintelligence

New players are rapidly gaining higher market share by attracting customers with digital products and innovative services, many of which are made possible through machine learning and artificial intelligence. Such players can easily meet customer demand as they already have lean and agile digital operations. To maintain leadership, incumbents must keep pace with the new normal of rapid disruption. With its capacity to learn from large datasets and establish patterns and correlations, machine learnings can revolutionize operations. It can inject new efficiencies into tasks such as risk assessment, fraud detection, anti-money laundering, trading, and customer service by providing instant insights, relevant recommendations, and informed decisions in real-time.


Feature Visualization

@machinelearnbot

How can we chose a preconditioner that will give us these benefits? A good first guess is one that makes your data decorrelated and whitened. In the case of images this means doing gradient descent in the Fourier basis, This points to a profound fact about the Fourier transform. As long as a correlation is consistent across spatial positions -- such as the correlation between a pixel and its left neighbor being the same across all positions of an image -- the Fourier coefficients will be independent variables. To see this, note that such a spatially consistent correlation can be expressed as a convolution, and by the convolution theorem becomes pointwise multiplication after the Fourier transform.


Feature Visualization

#artificialintelligence

How can we chose a preconditioner that will give us these benefits? A good first guess is one that makes your data decorrelated and whitened. In the case of images this means doing gradient descent in the Fourier basis, This points to a profound fact about the Fourier transform. As long as a correlation is consistent across spatial positions -- such as the correlation between a pixel and its left neighbor being the same across all positions of an image -- the Fourier coefficients will be independant variables. To see this, note that such a spatially consistent correlation can be expressed as a convolution, and by the convolution theorem becomes pointwise multiplication after the Fourier transform.


AllAnalytics - Pierre DeBois - Clustering: Knowing Which Birds Flock Together

@machinelearnbot

But suppose every piece is the same shape and is small enough to make images confusing at first look. You'd take a guess at how they fit, right? Data can be that way. Fortunately, analysts are finding many advanced ways to bring data together. One technique receiving attention these days is clustering, an unsupervised machine learning method that calculates how unlabeled data should be grouped.


Linear Regression in Python; Predict The Bay Area's Home Prices

#artificialintelligence

I chose the Bay Area housing price dataset that was sourced from Bay Area Home Sales Database and Zillow. This dataset was based on the homes sold between January 2013 and December 2015. It has many characteristics of learning. The dataset can be downloaded from here. There are several features that we do not need, such as "info", "z_address", "zipcode"(We have "neighborhood" as a location variable), "zipid" and "zestimate"(This is the price estimated by Zillow, we don't want our model to be affected by this).


Higgs boson uncovered by quantum algorithm on D-Wave machine

@machinelearnbot

Machine learning has returned with a vengeance. I still remember the dark days of the late '80s and '90s, when it was pretty clear that the current generation of machine-learning algorithms didn't seem to actually learn much of anything. Then big data arrived, computers became chess geniuses, conquered Go (twice), and started recommending sentences to judges. In most of these cases, the computer had sucked up vast reams of data and created models based on the correlations in the data. But this won't work when there aren't vast amounts of data available.


Will Our Robots Harm Us?

@machinelearnbot

Summary: We are approaching a time when we need to be concerned that our AI robots may indeed harm us. The rapid increase in the conversation about what ethics should apply to AI is appropriate but needs to be focused on the real threats, not just the wild imaginings of the popular press. Here are some data points to help you in thinking about this, what our concerns should be today, and what our concerns should be in the future. If you are a data scientist reading this the answer may seem obvious. And yet there is recently an explosion of institutes, conferences, and articles devoted to the ethics and ethical implications of artificial intelligence that imply by their very existence that this plausible.. Ethics is the branch of philosophy that seeks to define or recommend right and wrong conduct.


Deep learning vs. machine learning: The difference starts with data

#artificialintelligence

The answer to the question of what makes deep learning different from traditional machine learning for predictive... You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.