Spatial Analysis: Mapping Earthquakes in Japan, Korea, and China from 1970 to 2013
http://nbviewer.jupyter.org/github/Prooffreader/Misc_ipynb/blob/master/Japan_Earthquakes/earthquakes-jp.ipynb

/r/JupyterNotebooks
https://redd.it/47thtw

What's the state of django 3rd party libraries vs. rails?

Hi all,

I'm looking to build a responsive website with a handful of standard features, but really want to be able to leverage 3rd party frameworks and libraries to keep my life simple.

I'd far prefer to use python/django over rails if possible, but would go with whichever platform has more plug-and-play type functionality and have CLEAN widget design. I don't want to come up with a bunch of new UI element iconography.

Here are the main library types I'd be looking for:

Not sure on:

- Image upload & thumbnailing with cropping (to a CDN)
- Chat/messaging
- Forum type discussion
- Authentication via social media (FB)
- Search (by username and other custom filters)
- Payments (probably just stripe or braintree to start)

Probably standard:

- Autocomplete for search
- Easy form validation
- Captcha / spam prevention


/r/django
https://redd.it/5k9v2f

Watermark - An IPython magic extension for adding date and time stamps, version numbers, and hardware information to your notebooks
https://github.com/rasbt/watermark

/r/JupyterNotebooks
https://redd.it/47sifn

Where do you find Jypyter plugins?

Is there a central repo to search for plugins? I imagine pip might be a place, but it doesn't seem like all plugins are pip installable.

/r/IPython
https://redd.it/5kf6ve

meza: A Python toolkit for processing tabular data
https://github.com/reubano/meza

/r/pystats
https://redd.it/4223qq

Heavyweight Match: Survival Analysis in R vs Python
https://plot.ly/ipython-notebooks/survival-analysis-r-vs-python/

/r/JupyterNotebooks
https://redd.it/47rrm4

[D] Machine Learning - WAYR (What Are You Reading) - Week 16

This is a place to share machine learning research papers, journals, and articles that you're reading this week.
If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read.

Please try to provide some insight from your understanding and please don't post things which are present in wiki.

Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links.

|Previous weeks|
|--------------|
|[Week 1](https://www.reddit.com/r/MachineLearning/comments/4qyjiq/machine_learning_wayr_what_are_you_reading_week_1/)|
|[Week 2](https://www.reddit.com/r/MachineLearning/comments/4s2xqm/machine_learning_wayr_what_are_you_reading_week_2/)|
|[Week 3](https://www.reddit.com/r/MachineLearning/comments/4t7mqm/machine_learning_wayr_what_are_you_reading_week_3/)|
|[Week 4](https://www.reddit.com/r/MachineLearning/comments/4ub2kw/machine_learning_wayr_what_are_you_reading_week_4/)|
|[Week 5](https://www.reddit.com/r/MachineLearning/comments/4xomf7/machine_learning_wayr_what_are_you_reading_week_5/)|
|[Week 6](https://www.reddit.com/r/MachineLearning/comments/4zcyvk/machine_learning_wayr_what_are_you_reading_week_6/)|
|[Week 7](https://www.reddit.com/r/MachineLearning/comments/52t6mo/machine_learning_wayr_what_are_you_reading_week_7/)|
|[Week 8](https://www.reddit.com/r/MachineLearning/comments/53heol/machine_learning_wayr_what_are_you_reading_week_8/)|
|[Week 9](https://www.reddit.com/r/MachineLearning/comments/54kvsu/machine_learning_wayr_what_are_you_reading_week_9/)|
|[Week 10](https://www.reddit.com/r/MachineLearning/comments/56s2oa/discussion_machine_learning_wayr_what_are_you/)|
|[Week 11](https://www.reddit.com/r/MachineLearning/comments/57xw56/discussion_machine_learning_wayr_what_are_you/)|
|[Week 12](https://www.reddit.com/r/MachineLearning/comments/5acb1t/d_machine_learning_wayr_what_are_you_reading_week/)|
|[Week 13](https://www.reddit.com/r/MachineLearning/comments/5cwfb6/d_machine_learning_wayr_what_are_you_reading_week/)|
|[Week 14](https://www.reddit.com/r/MachineLearning/comments/5fc5mh/d_machine_learning_wayr_what_are_you_reading_week/)|
|[Week 15](https://www.reddit.com/r/MachineLearning/comments/5hy4ur/d_machine_learning_wayr_what_are_you_reading_week/)|

Most upvoted papers last week :

[Learning to learn by gradient descent by gradient descent](https://arxiv.org/abs/1606.04474)

[Natural Language Understanding with Distributed Representation](https://github.com/nyu-dl/NLP_DL_Lecture_Note)

[Geometric deep learning: going beyond Euclidean data](https://arxiv.org/abs/1611.08097)

Besides that, there are no rules, have fun and Merry Christmas to everyone!

/r/MachineLearning
https://redd.it/5kd6vd

mitmproxy (HTTPS Debugger) 1.0 released, now Python 3 only and with a web interface
https://corte.si/posts/code/mitmproxy/announce_1_0/index.html

/r/Python
https://redd.it/5kev64

Django vS other Dev frameworks

Hi can someone explain to me in laymens terms the perks of developing applications quickly within the django framework as opposed to writing logic by hand with JS and using that to interact with my html/css? I am currently working with python for my applications due to it's ease of transferring ideas to code. However I am still unsure about how to make the GUI/front-end is that all still done with standard HTML/css and django/python is back-end/database interaction ? Would love to get a discussion going, hope everyones having a good boxing day.

/r/djangolearning
https://redd.it/5ke7cg

PrettyPandas - Add report style summaries and units to Pandas DataFrames
http://prettypandas.readthedocs.org/en/latest/

/r/pystats
https://redd.it/41rko0

Making Python Really Fast
https://medium.com/@paulcolomiets/making-python-really-fast-6407679c9a3d#.reh5m7pzp

/r/Python
https://redd.it/5kf0qp

adjustText - Automatically adjust text position on matplotlib graphs to minimize overlaps
https://github.com/Phlya/adjustText

/r/pystats
https://redd.it/40t417

The map

/r/IPython
https://redd.it/5khf6c

alot of .PY scripts I found. Dont know what they do but thought somebody here would like to look over them.
http://cage.owltux.com/?dir=script/script%20python

/r/Python
https://redd.it/5ki1ze

Pythonistas: do you ever find the need to use Perl?

Similar question: if you learned Perl first and Python later, do you ever use Perl?

These questions came up in this thread at /r/perl:
https://www.reddit.com/r/perl/comments/5jvsr6/how_does_one_convince_pythonistas_that_perl_is/?

/r/Python
https://redd.it/5ki1tc

(Cross-post from /r/python) neat-python v0.6 release: evolution of arbitrary neural networks
https://pypi.python.org/pypi/neat-python/0.6

/r/pystats
https://redd.it/40csw6

Then

/r/IPython
https://redd.it/5khc6b

I accidentally uploaded my secret key onto a public github repo for a trivial practice app--am I under any serious threat for anything?

It was a side project I did for practice, but would like to keep it on github to keep record of my progress.

What sort of risk am I taking in making my secret key available?

side question: when uploading a django app onto GH, what sort of security measures should I take from now on? What belongs in the .gitignores of django apps?

/r/django
https://redd.it/5kix2v

[D] How to train generative adversarial networks

We have had a few posts here before, and the [notes of the talk at NIPS](https://github.com/soumith/ganhacks) are useful, and there are some other great "how to" [resources](https://github.com/shekkizh/neuralnetworks.thought-experiments/blob/master/Generative%20Models/GAN/Readme.md). There is also the [Torch blogpost on the topic](http://torch.ch/blog/2015/11/13/gan.html).

I have generated images that look good. I have toyed with tons of GAN variants.

But I still feel like I have no idea how to train a GAN in any sort of rigorous manner. To me it seems like the most important factor in generating visually understandable images is some strange and exploratory architecture balancing act, which appears completely arbitrary. I train one GAN to equilibrium and end up with trash images, but change a tiny thing in the architecture and reach a very similar equilibrium (ie the G and D training curves look the same) but have nice images.

This suggests to me that what we are actually doing is (over)fitting models based on human visual interpretation, not model metrics. This is very "human in the loop" and not theoretically satisfying. And more frustratingly, it is not discussed even in the few articles and blogposts on GAN hacks and methods. I wasn't at NIPS so I don't know if the workshop covered more than the git.

Mode collapse and "stability" don't seem to explain my experiences. It kind of feels more like model hacking. I know my colleagues feel similar frustrations, like we are all just randomly wandering around image space until we stumble upon a training method that lands us close to the image manifold and then it magically starts working.

I really want to be told I am just missing something that should be obvious.

So, is there any rigorous way to train a GAN? Where I can look at my curves and my gradients and my activations and just *know* I will end up with quality images?



/r/MachineLearning
https://redd.it/5kj566

What's everyone working on this week?

Tell /r/python what you're working on this week! You can be bragging, grousing, sharing your passion, or explaining your pain. Talk about your current project or your pet project; whatever you want to share.


/r/Python
https://redd.it/5kjv9n