Hi yall! I wrote a post about Machine Learning, Data Science, 5G and IoT. Would you mind to give it a read? I would love to hear feedback from you guys!

Here’s the link:

https://lautarolobo.xyz/blog/how-machine-learning-5g-and-data-science-will-be-critical-to-the-future-of-the-internet-of-things/

Tell me anything! ‘Bout the site, the content, writing. Is it too nerdy? Too boring? Do you think that Machine Learning encryption will gain more popularity over time?

Hello,

Was wondering if anybody had ever done this. I’ve searched quite a bit but haven’t come up with anything.

I have a bunch of hotel data with different features (amenities like a pool, workout room, number of rooms, a whole bunch of others) as well as a bunch of results of people who chose hotel A vs B, etc. I need to figure out which “features” played prominently in people choosing certain hotels over others. Classic customer choice.

Not sure a discrete choice model is the best for this, though I’m exploring it- but, essentially, I’m trying to figure out if I can approach this as a supervised learning problem in order to figure out which of these features figure most highly into choices of hotels.

Having looked at classifier feature importance in the past on a lot of projects (mainly xgboost) if I were able to get creative and properly vectorize lists of features in my input set between hotels, in a way that would allow me to train a classifier on the choices, I could then look at which features were most important as a measure of choice forecasting.

Assuming the data has cohesive predictive characteristics, would this approach make sense? Or is this a bastardization of how feature importance is supposed to work?

Hi guys,

I’m a newbie to machine learning and this is my first time posting so I’m kind of nervous… This is my implementation of a research paper regarding an intrusion detection system using pytorch. Please give some feedback and suggestions on what can be added.

Repo: https://github.com/jaloo555/ids_dl

Research Paper: https://eudl.eu/pdf/10.4108/eai.3-12-2015.2262516

On a side note: It would be amazing if anyone would like to join me in bringing this into a demo webapp showing intrusion statistics!

A new article on distill pub with interactive visualizations on value learning:

The Paths Perspective on Value Learning

A closer look at how Temporal Difference learning merges paths of experience for greater statistical efficiency.

https://distill.pub/2019/paths-perspective-on-value-learning/

Looking at Kaggle results, the top entries are so close together in points that the difference starts to look suspiciously like noise.

Are there any instances of a top entry in a major challenge completely blowing other models out of the water?

The day has finally come, go grab it here:

https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0

I’ve been using it since it was in alpha stage and I’m very satisfied with the improvements and new additions.

I am an astrophysics student in Toronto who are doing my PhD project something about deep learning on astrophysical data. My supervisor advised me to apply for NeurIPS this year to chat and learn with other people in different field. I know NeurIPS changed the ticket system to a lottery. But I still have difficulty to figure it out the whole process.

So I did join the lottery and received an email with link to get a “reserved ticket” during the past weekend. I don’t know what does it mean? Like I actually get the ticket and I just have to complete the process and paid?

Moreover, I am especially interested in the workshop ” Machine Learning and the Physical Sciences”. But it does not seems like I can submit my work/poster to them, or even apply to the workshop. I can only apply to workshops after I get my ticket or I already missed the deadline? Thanks

Hello

To be frank, I need a piece of advice. I intend to run a long task (preferably within a Jupyter Notebook) bound mainly by CPU. More specifically I want to run TPOT on a fairly large visual dataset. Now, as I do no physically own any machine suited for this kind of endeavor, I resort to the cloud. And, as I have some experience with AWS, I instinctively opted for AWS SageMaker to host some JupyterLab instance. One pain-point most of you have already experience is the fact that you need to keep the JupyerLab (or Notebook) window open at all times with a working connection in order not to lose the cell outputs. Keeping all that in mind, I left my computer open while I was gone for a couple of hours only to come back and realize my AWS auth token expired automatically and I was logged out from their platform. This also invalidated the connection with SageMaker notebook instance and made me conscious of all that hosting money that went down the drain.

Anyway, how do you guys run your ML tasks? Especially those on Jupyter Notebooks and CPU-intensive?

Side note: did anyone experiment with an EC2 instance hosting their Jupyter instance & not relying on AWS’ authentication?

There has been pretty much zero talk/experiments of the new CTRL model, so I played around with it myself, and found that it could generate fake news convincingly! Certainly much better than GPT-2 in that area.

https://minimaxir.com/2019/09/ctrl-fake-news/

ICYMI, there’s now also a Colaboratory Notebook for CTRL, although your mileage may vary.