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Machine Learning with Python: Easy and robust method to fit nonlinear data ☞ https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49

At $30, the Aryzon augmented reality headset may be the cheapest of its kind. Like Google’s cardboard VR headset, it lets you interact with holograms.

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Michael Conover: Information Visualization for Large-Scale Data Workflows

data geometry

memes

visual analysis of program structure

visual analysis of propaganda

compare last week’s analysis and share with colleagues

geom_bin2d rather than geom_point(alpha=...) in ggplot2

ggpairs

automated grading: in addition to unit testing, 1) parse syntax trees of submissions, 2) define edit distance between them, 3) induces a network structure, 4) identify clusters, 5) give feedback to a representative member of the cluster and cc: everyone else

Presented at SF Data Mining on Oct 9, 2013 The ability to instrument and interrogate data as it moves through a processing pipeline is fundamental to effecti… @vagabondjack reasonengine.wordpress.com

Photo-editing app FaceApp now includes Black, Asian Indian and Caucasian filters

On Wednesday morning, the photo-editing app FaceApp released new photo filters that change the ethnic appearance of your face.

The app first became popular earlier in 2017 due to its ability to transform people into elderly versions of themselves and different genders.

These new options, however, will likely cause some outrage: The filters are Asian, Black, Caucasian and Indian.

Selfie apps like Snapchat have taken criticism for filters that apply “digital blackface.” In 2016, Snapchat released a Bob Marley filter that darkened the skin and gave users dreadlocks. Snapchat said another one of its 2016 filters was “inspired by anime,” but many people called it “yellowface,” as it seemingly turned the user into an Asian stereotype.

FaceApp’s newest filters, however, don’t pretend they’re anything but racial. Read more (8/9/17 12 PM)

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Introducing SAMOA, an open source platform for mining big data streams.

https://github.com/yahoo/samoa

Machine learning and data mining are well established techniques in the world of IT and especially among web companies and startups. Spam detection, personalization and recommendations are just a few of the applications made possible by mining the huge quantity of data available nowadays. However, “big data” is not only about Volume, but also about Velocity (and Variety, 3V of big data).

The usual pipeline for modeling data (what “data scientists” do) involves taking a sample from production data, cleaning and preprocessing it to make it usable, training a model for the task at hand and finally deploying it to production. The final output of this process is a pipeline that needs to run periodically (and be maintained) in order to keep the model up to date. Hadoop and its ecosystem (e.g., Mahout) have proven to be an extremely successful platform to support this process at web scale.

However, no solution is perfect and big data is “data whose characteristics forces us to look beyond the traditional methods that are prevalent at the time”. The current challenge is to move towards analyzing data as soon as it arrives into the system, nearly in real-time.

For example, models for mail spam detection get outdated with time and need to be retrained with new data. New data (i.e., spam reports) comes in continuously and the model starts being outdated the moment it is deployed: all the new data is sitting without creating any value until the next model update. On the contrary, incorporating new data as soon as it arrives is what the “Velocity” in big data is about. In this case, Hadoop is not the ideal tool to cope with streams of fast changing data.

Distributed stream processing engines are emerging as the platform of choice to handle this use case. Examples of these platforms are Storm, S4, and recently Samza. These platforms join the scalability of distributed processing with the fast response of stream processing. Yahoo has already adopted Storm as a key technology for low-latency big data processing.

Alas, currently there is no common solution for mining big data streams, that is, for doing machine learning on streams on a distributed environment.

Enter SAMOA

SAMOA (Scalable Advanced Massive Online Analysis) is a framework for mining big data streams. As most of the big data ecosystem, it is written in Java. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm and S4. SAMOA includes distributed algorithms for the most common machine learning tasks such as classification and clustering. For a simple analogy, you can think of SAMOA as Mahout for streaming.

SAMOA is both a platform and a library. As a platform, it allows the algorithm developer to abstract from the underlying execution engine, and therefore reuse their code to run on different engines. It also allows to easily write plug-in modules to port SAMOA to different execution engines.

As a library, SAMOA contains state-of-the-art implementations of algorithms for distributed machine learning on streams. The first alpha release allows classification and clustering.

For classification, we implemented a Vertical Hoeffding Tree (VHT), a distributed streaming version of decision trees tailored for sparse data (e.g., text). For clustering, we included a distributed algorithm based on CluStream. The library also includes meta-algorithms such as bagging.

HOW DOES IT WORK?

An algorithm in SAMOA is represented by a series of nodes communicating via messages along streams that connect pairs of nodes (a graph). Borrowing the terminology from Storm, this is called a Topology. Each node in the Topology is a Processor that sends messages to a Stream. The user code that implements the algorithm resides inside a Processor. Figure 3 shows an example of a Processor joining two stream from two source Processors. Here is a code snippet to build such a topology in SAMOA.

TopologyBuilder builder; Processor sourceOne = new SourceProcessor(); builder.addProcessor(sourceOne); Stream streamOne = builder.createStream(sourceOne); Processor sourceTwo = new SourceProcessor(); builder.addProcessor(sourceTwo); Stream streamTwo = builder.createStream(sourceTwo); Processor join = new JoinProcessor(); builder.addProcessor(join).connectInputShuffle(streamOne).connectInputKey(streamTwo);

SWEET! HOW DO I GET STARTED?

1. Download SAMOA

git clone git@github.com:yahoo/samoa.git cd samoa mvn -Pstorm package

2. Download the Forest CoverType dataset.

wget "http://downloads.sourceforge.net/project/moa-datastream/Datasets/Classification/covtypeNorm.arff.zip" unzip covtypeNorm.arff.zip

Forest CoverType contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. It contains 581,012 instances and 54 attributes, and it has been used in several papers on data stream classification.

3. Download a simple logging library.

wget "http://repo1.maven.org/maven2/org/slf4j/slf4j-simple/1.7.2/slf4j-simple-1.7.2.jar"

4. Run an Example. Classifying the CoverType dataset with the VerticalHoeffdingTree in local mode.

java -cp slf4j-simple-1.7.2.jar:target/SAMOA-Storm-0.0.1.jar com.yahoo.labs.samoa.DoTask "PrequentialEvaluation -l classifiers.trees.VerticalHoeffdingTree -s (ArffFileStream -f covtypeNorm.arff) -f 100000"

The output will be a sequence of the evaluation metrics for accuracy, taken every 100,000 instances.

To run the example on Storm, please refer to the instructions on the wiki.

I WANT TO KNOW MORE!

For more information about SAMOA, see the README and the wiki on github, or post a question on the mailing list.

SAMOA is licensed under an Apache Software License v2.0. You are welcome to contribute to the project! SAMOA accepts contributions under an Apache style contributor license agreement.

Good luck! We hope you find SAMOA useful. We will continue developing the framework by adding new algorithms and platforms.

Gianmarco De Francisci Morales (gdfm@yahoo-inc.com) and Albert Bifet (abifet@yahoo.com) @ Yahoo Labs Barcelona

Webb 101: 10 Facts about the James Webb Space Telescope

Did you know…?

1. Our upcoming James Webb Space Telescope will act like a powerful time machine – because it will capture light that’s been traveling across space for as long as 13.5 billion years, when the first stars and galaxies were formed out of the darkness of the early universe.

2. Webb will be able to see infrared light. This is light that is just outside the visible spectrum, and just outside of what we can see with our human eyes.

3. Webb’s unprecedented sensitivity to infrared light will help astronomers to compare the faintest, earliest galaxies to today’s grand spirals and ellipticals, helping us to understand how galaxies assemble over billions of years.

Hubble’s infrared look at the Horsehead Nebula. Credit: NASA/ESA/Hubble Heritage Team

4. Webb will be able to see right through and into massive clouds of dust that are opaque to visible-light observatories like the Hubble Space Telescope. Inside those clouds are where stars and planetary systems are born.

5. In addition to seeing things inside our own solar system, Webb will tell us more about the atmospheres of planets orbiting other stars, and perhaps even find the building blocks of life elsewhere in the universe.

Credit: Northrop Grumman

6. Webb will orbit the Sun a million miles away from Earth, at the place called the second Lagrange point. (L2 is four times further away than the moon!)

7. To preserve Webb’s heat sensitive vision, it has a ‘sunshield’ that’s the size of a tennis court; it gives the telescope the equivalent of SPF protection of 1 million! The sunshield also reduces the temperature between the hot and cold side of the spacecraft by almost 600 degrees Fahrenheit.

8.  Webb’s 18-segment primary mirror is over 6 times bigger in area than Hubble’s and will be ~100x more powerful. (How big is it? 6.5 meters in diameter.)

9.  Webb’s 18 primary mirror segments can each be individually adjusted to work as one massive mirror. They’re covered with a golf ball’s worth of gold, which optimizes them for reflecting infrared light (the coating is so thin that a human hair is 1,000 times thicker!).

10. Webb will be so sensitive, it could detect the heat signature of a bumblebee at the distance of the moon, and can see details the size of a US penny at the distance of about 40 km.

BONUS!  Over 1,200 scientists, engineers and technicians from 14 countries (and more than 27 U.S. states) have taken part in designing and building Webb. The entire project is a joint mission between NASA and the European and Canadian Space Agencies. The telescope part of the observatory was assembled in the world’s largest cleanroom at our Goddard Space Flight Center in Maryland.

Webb is currently being tested at our Johnson Space Flight Center in Houston, TX.

Afterwards, the telescope will travel to Northrop Grumman to be mated with the spacecraft and undergo final testing. Once complete, Webb will be packed up and be transported via boat to its launch site in French Guiana, where a European Space Agency Ariane 5 rocket will take it into space.

Learn more about the James Webb Space Telescope HERE, or follow the mission on Facebook, Twitter and Instagram.

Make sure to follow us on Tumblr for your regular dose of space: http://nasa.tumblr.com.

Eclipse Across America

August 21, 2017, the United States experienced a solar eclipse! 

An eclipse occurs when the Moon temporarily blocks the light from the Sun. Within the narrow, 60- to 70-mile-wide band stretching from Oregon to South Carolina called the path of totality, the Moon completely blocked out the Sun’s face; elsewhere in North America, the Moon covered only a part of the star, leaving a crescent-shaped Sun visible in the sky.

During this exciting event, we were collecting your images and reactions online. 

Here are a few images of this celestial event…take a look:

This composite image, made from 4 frames, shows the International Space Station, with a crew of six onboard, as it transits the Sun at roughly five miles per second during a partial solar eclipse from, Northern Cascades National Park in Washington. Onboard as part of Expedition 52 are: NASA astronauts Peggy Whitson, Jack Fischer, and Randy Bresnik; Russian cosmonauts Fyodor Yurchikhin and Sergey Ryazanskiy; and ESA (European Space Agency) astronaut Paolo Nespoli.

Credit: NASA/Bill Ingalls

The Bailey’s Beads effect is seen as the moon makes its final move over the sun during the total solar eclipse on Monday, August 21, 2017 above Madras, Oregon.

Credit: NASA/Aubrey Gemignani

This image from one of our Twitter followers shows the eclipse through tree leaves as crescent shaped shadows from Seattle, WA.

Credit: Logan Johnson

“The eclipse in the palm of my hand”. The eclipse is seen here through an indirect method, known as a pinhole projector, by one of our followers on social media from Arlington, TX.

Credit: Mark Schnyder

Through the lens on a pair of solar filter glasses, a social media follower captures the partial eclipse from Norridgewock, ME.

Credit: Mikayla Chase

While most of us watched the eclipse from Earth, six humans had the opportunity to view the event from 250 miles above on the International Space Station. European Space Agency (ESA) astronaut Paolo Nespoli captured this image of the Moon’s shadow crossing America.

Credit: Paolo Nespoli

This composite image shows the progression of a partial solar eclipse over Ross Lake, in Northern Cascades National Park, Washington. The beautiful series of the partially eclipsed sun shows the full spectrum of the event. 

Credit: NASA/Bill Ingalls

In this video captured at 1,500 frames per second with a high-speed camera, the International Space Station, with a crew of six onboard, is seen in silhouette as it transits the sun at roughly five miles per second during a partial solar eclipse, Monday, Aug. 21, 2017 near Banner, Wyoming.

Credit: NASA/Joel Kowsky

To see more images from our NASA photographers, visit: https://www.flickr.com/photos/nasahqphoto/albums/72157685363271303

Make sure to follow us on Tumblr for your regular dose of space: http://nasa.tumblr.com

“ Promise you won’t leave without me.”

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Inside the Blockchain Factory: How IBM's Distributed Ledger Work Went Global

IBM is building its blockchain work over a growing number of locations and employees, and Marie Wieck ties it all together. from CoinDesk http://ift.tt/2xbXrkC Donate Bitcoins 191LaSo6DsQFFMr9NQjyHBeYKLogfEYkBa