Data Science

it is playlist about machine learning and artificial intelligence. I'm putting here all podcast in english in related topic. If you know something that should be here please drop me a message with link and follow me on twitter: @hyzhak_en

artificial intelligence

Machine Learning & Data Science

Linear Digressions - Old to New

Linear Digressions

Good stuff

Machine learning and particle physics go together like peanut butter and jelly--but this is a relatively new development.  

For many decades, physicists looked through their fairly large datasets using the laws of physics to guide their exploration; that tradition continues today, but as ever-larger datasets get made, machine learning becomes a more tractable way to deal with the deluge.  

With this in mind, ATLAS (one of the major experiments at CERN, the European Center for Nuclear Research and home laboratory of the recently discovered Higgs boson) ran a machine learning contest over the summer, to see what advances could be found from opening up the dataset to non-physicists.  

The results were impressive--physicists are smart folks, but there’s clearly lots of advances yet to make as machine learning and physics learn from one another.  And who knows--maybe more Nobel prizes to win as well!

https://www.kaggle.com/c/higgs-boson

Hunting for the Higgs

In the 1850s, there were a lot of things we didn’t know yet: how to create an airplane, how to split an atom, or how to control the spread of a common but deadly disease: cholera.  

When a cholera outbreak in London killed scores of people, a doctor named John Snow used it as a chance to study whether the cause might be very small organisms that were spreading through the water supply (the prevailing theory at the time was miasma, or “bad air”).  By tracing the geography of all the deaths from the outbreak, Snow was practicing elementary data science--and stumbled upon one of history’s most famous outliers.  

In this episode, we’ll tell you more about this single data point, a case of cholera that cracked the case wide open for Snow and provided critical validation for the germ theory of disease.

http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak

How Outliers Helped Defeat Cholera

With the rise of mobile phones equipped with GPS, we're meeting many more data scientists at start-ups and large companies who specialize in spatio-temporal pattern recognition. Analyzing “moving dots” requires specialized tools and techniques. A few months ago, I sat down with Rajiv Maheswaran founder and CEO of Second Spectrum, a company that applies analytics to sports tracking data. Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.

For more on our interview, visit O'Reilly Radar: http://oreil.ly/1t7EcOQ

The science of moving dots

Computers are capable of many impressive feats, but making you laugh is usually not one of them. Or could it be? This episode will talk about a custom-built machine learning algorithm that searches through text and writes jokes based on what it finds. 

The jokes are formulaic: they're all of the form "I like my X like I like my Y: Z" where X and Y are nouns, and Z is an adjective that can describe both X and Y. For (dumb) example, "I like my men like I like my coffee: steaming hot." The joke is funny when ZX and ZY are both very common phrases, but X and Y are rarely seen together. 

So, given a large enough corpus of text, the algorithm looks for triplets of words that fit this description and writes jokes based on them. Are the jokes funny? You be the judge...

http://homepages.inf.ed.ac.uk/s0894589/petrovic13unsupervised.pdf

Computers Try to Tell Jokes

In this episode, Ben Lorica chats with Jay Kreps, a highly regarded data engineer and former technical lead for Online Data Infrastructure at LinkedIn, and most recently CEO/co-founder of Confluent, about Apache Kafka, data integration, event data, and the Internet of Things.

Building Apache Kafka From Scratch

Baseball is characterized by a high level of equality between teams; even the best teams might only have 55% win percentages (contrast this with college football, where teams go undefeated pretty regularly). In this regime, where 2 outcomes (Giants win/Giants lose) are approximately equally likely, we can model the win/loss chances with a binomial distribution. 

Using the binomial distribution, we can calculate an interesting little result: what's the chance of the world series going to only 4 games? 5? 6? All the way to 7? Then we can compare to decades' worth of world series data, to see how well the data follows the binomial assumption. 

The result tells us a lot about sports psychology--if each game is independent of the others, 4/5/6/7 game series are equally likely. The data shows a different trend: 4 and 7 game series are significantly more likely than 5 or 6. There's a powerful psychological effect at play--everybody loves the 7th game of the world series, or a good sweep. And it turns out that the baseball teams, whether they intend it or not, oblige our love of short (4) and long (7) world series!

http://blog.philbirnbaum.com/2007/06/winning-world-series-in-x-games.html

Stats of World Series Streaks

O'Reilly Ben Lorica chats with Ion Stoica, UC Berkeley Professor and Databricks CEO, about the rise of Apache Spark and Apache Mesos.

Apache Spark's Journey From Academia To Industry

A true classic topic in ML: Facial recognition is very high-dimensional, meaning that each picture can have millions of pixels, each of which can be a single feature. It's computationally expensive to deal with all these features, and invites overfitting problems. PCA (principal components analysis) is a classic dimensionality reduction tool that compresses these many dimensions into the few that contain the most variation in the data, and those principal components are often then fed into a classic ML algorithm like and SVM. 

One of the best thing about eigenfaces is the great example code that you can find in sklearn--you can distinguish pictures of world leaders yourself in just a few minutes!

http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html

Facial Recognition with Eigenfaces

O’Reilly’s big data guru Ben Lorica sits down with prominent data scientist DJ Patil to talk about his new ebook (written with Hilary Mason) and other topics in data science and big data.

A Brief Look At Data Science's Past And Future

There is no known learning algorithm that is more flexible and powerful than the human brain. That's quite inspirational, if you think about it--to level up machine learning, maybe we should be going back to biology and letting millions of year of evolution guide the structure of our algorithms. 

This is the idea behind neural nets, which mock up the structure of the brain and are some of the most studied and powerful algorithms out there. In this episode, we’ll lay out the building blocks of the neural net (called neurons, naturally) and the networks that are built out of them. 

We’ll also explore the results that neural nets get when used to do object recognition in photographs.

Neural Nets (Part 1)

Ever found yourself wasting time reading online comments from trolls?  Of course you have; we've all been there (it's 4 AM but I can't turn off the computer and go to sleep--someone on the internet is WRONG!).  Now there's a way to use machine learning to automatically detect trolls, and minimize the impact when they try to derail online conversations.

Troll Detection

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben