Bayesian


Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?

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We could not so far claim that deep networks trained with stochastic gradient descent are Bayesian. And it may be because SGD biases learning towards flat minima, rather than sharp minima. It turns out, (Hochreiter and Schmidhuber, 1997) motivated their work on seeking flat minima from a Bayesian, minimum description length perspective. Seeking flat minima makes sense from a minimum description length perspective.


Two meanings of priors, part I: The plausibility of models

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However, it may not be intuitively clear that the meaning of "prior" differs in these terms. In fact, there are two meanings of "prior" in the context of Bayesian […] The post Two meanings of priors, part I: The plausibility of models appeared first on Nicebread. The post Two meanings of priors, part I: The plausibility of models appeared first on All About Statistics.


Bayesian Basics, Explained

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Editor's note: The following is an interview with Columbia University Professor Andrew Gelman conducted by Marketing scientist Kevin Gray, in which Gelman spells out the ABCs of Bayesian statistics. Andrew Gelman: Bayesian statistics uses the mathematical rules of probability to combines data with "prior information" to give inferences which (if the model being used is correct) are more precise than would be obtained by either source of information alone. Classical statistical methods avoid prior distributions. In classical statistics, you might include in your model a predictor (for example), or you might exclude it, or you might pool it as part of some larger set of predictors in order to get a more stable estimate. These are pretty much your only choices.


Bayesian Approach for Sales Time Series Forecasting

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In our previous post, we showed the examples of using linear models and machine learning approach for forecasting sales time series. Sometimes we need to forecast not only more probable values of sales but also their distribution. Especially we need it in the risk analysis for assessing different risks related to sales dynamics. In this case we need to take into account sales distributions and dependencies between sales time series features (e.g. One can consider sales as a stochastic variable with some marginal distributions.


8 Data Science Skills That Every Employee Needs

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Analytics is about getting your team the data insights it needs to build better products and make the right decisions for your company. But if your team can't understand that data, then this is all for naught. Software like Amplitude can make your data easy to understand, but each member of your team still needs basic data skills to get the most value out of what they're looking at. These skills can help your team, regardless of whether that's in product or marketing or sales, interpret the data as it comes in. It also gives them the skills to work with your data scientists to propose new ideas for your product, as well as the confidence to work alongside them to improve the business.


NIPS 2016 -- Day 3 Highlights: Robots that know, Cars that see, and more!

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Day 3 Highlights: Robots that know, Cars that see, and more! Check out Day 1 and Day 2. Want to learn about applied Artificial Intelligence from leading practitioners in Silicon Valley or New York? Or rather, the robots are poking? One of the emerging themes of Day 3 was deep learning integrated into hardware applications, specifically robots and cars. Of these, a standout talk was given by Pulkit Agrawal about his team's work Learning to Poke by Poking: Experiential Learning of Intuitive Physics.


Bayesian Machine Learning in Python: A/B Testing

@machinelearnbot

I am a data scientist, big data engineer, and full stack software engineer. For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers. I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.


50 Free Data Science Books

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Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors - usually academics - are more prone to making their books available for free). Many are O'Reilly books freely available. Here we display those most relevant to data science. I haven't checked all the sources, but they seem legit. If you find some issue, let us know in the comment section below.


50 Free Data Science Books

@machinelearnbot

Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors - usually academics - are more prone to making their books available for free). Many are O'Reilly books freely available. Here we display those most relevant to data science. I haven't checked all the sources, but they seem legit. If you find some issue, let us know in the comment section below.


Artificial intelligence has a lot to learn from babies

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This article originally appeared on the International Business Times. Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a 4-year-old can do, there's still a pretty enormous gap."