June meetup

[Titus-von-Koeller]

Let's schedule a meetup towards the end of June:

Speakers

• Tim Head (@betatim )
• Marcin Pietrzyk
• Michal Rachtan (@mrastan )

Date

• 26.06.2018

Location

• Digicomp

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Dear Zurich PyDatonians,

first of all, please subscribe to our new Twitter account!

we are looking to organize our next event around mid to end of June, maybe at latest early July.

We urgently need input and help in organizing some interesting speakers for us. I know everybody is crazy for machine learning,

but anything involving data and algorithms as well as their (hopefully interesting) applications, as well as CS education (part of our mission!) would be a potentially cool additional insight into the diverse field of computing. Other topics could be evolutionary algorithms & generative music (see below), robotics, life sciences, finance, you name it! Let us know and tag the respective people in this GitHub repo.

Also, we are hypothesizing the coolness of possibly organizing a weekend-long hackathon on generative music, somehow involving evolutionary algorithms and machine learning. So if anyone knows somebody with something useful to say on these two topics, that would be really great input. See the linked issue for the organization of the actual (sometime/maybe) hackathon, also regarding competing ideas for what to do.

Thanks all and looking forward to another great event!

Tim, Joel & Titus

[betatim]

I am giving a talk about mybinder.org at the Paris PyData meetup so I'd be happy to give a pre-/post-view of that talk in Zurich.

[mrastan]

Unit8 gave an interesting talk during Machine Learning meetup in Lausanne a engineering from good - using deep learning to detect pneumonia on x-ray images. Let me know if you are interested...

[lukaszbinden]

For a graduate semester project we did transfer learning experiments using the NIH chest X-rays dataset for predicting skeletal age on the Stanford medicine pediatric hand X-rays dataset. We could present that.

[jakeret]

@lukaszbinden this sounds interesting. Would Tue 26th work for you? If so, could you provide us a talk title and a short abstract?

[betatim]

Binder - one click sharing of your data science

When other people want to run the code of the cool data project you did last week you usually think: “Great someone cares!” and then “Oh no, now I need to play support desk till they get it running.”

The Binder project lets anyone run the contents of a git repository by clicking a link. For example try out the latest JupyterLab demo by clicking this link. Binder lets you describe the dependencies of your repository in a way that we can automatically create a Docker container from it. Removing the need for you to spend a lot of time to help others who are trying to get your code to run.

I will tell you about the Binder project, how to use it to share work, what the tools behind it are, and how you can join the team working on Binder.

Some example uses:

• Reproduce and explore the journal article "Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations" by Ross et al (https://mybinder.org/v2/gh/dtak/rrr/master?urlpath=lab).
• Learn about "Foundations of numerical computing" (https://mybinder.org/v2/gh/ssanderson/foundations-of-numerical-computing/master?filepath=notebooks) with Scott Sanderson.
• Dive into Julia Evans’ "Pandas cookbook" (https://mybinder.org/v2/gh/jvns/pandas-cookbook/master).

[lukaszbinden]

unfortunately on the 26th we cannot present as one of us is abroad in that week. A possibility would be within 20th-22nd of June, before that we have exams. Otherwise starting from September again, from then on quite anytime. Lukas

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[mrastan]

Opportunities and obstacles for AI adoption from the industry perspective (10 mins) - Dr Marcin Pietrzyk

In this talk we will discuss opportunities, obstacles and lessons learned from working with some of the most prominent non-digital native industries. We will unveil the biggest challenges the companies struggle with while adopting AI and factors that lead to success.

Engineering for good - using Deep Learning to detect pneumonia on X-Ray images (15-20 mins) - Michal Rachtan

Pneumonia is the world’s deadliest disease, killing two children every minute. Today, chest X-rays are the best known and available method for diagnosing pneumonia.

In this talk, we will highlight some of the challenges associated with diagnosing the disease and how machine learning can be employed to assist in the process. We will use Kaggle data to explain how the selected ML techniques and software including Convolution Neural Networks, Transfer Learning, and Tensorflow can be employed to detect the disease from raw X-Ray images. We look forward to an interactive session and to hear from you too.

[jakeret]

As you might have already seen I've scheduled the meetup for the 26th of june.

@lukaszbinden I would propose that we reschedule your talk to september (#44).

@mrastan would it be possible for Marcin to extend the talk a bit to ~20min?