Hi all,

https://github.com/OpenNMT/OpenNMT-tf

Just wanted to share this new major update of OpenNMT-tf, a toolkit for neural machine translation and sequence generation initially released in 2017. It has been completely redesigned for TensorFlow 2.0 and now includes many useful modules and layers that can be reused in other projects, from dataset utilities to beam search decoding.

Fully upgrading to TensorFlow 2.0 required some energy but we feel the changes are positive: the implementation is simpler, more consistent, and more extensible.

Please check it out, and feel free to ask questions!

Hi!

This is our new paper on capsule networks! https://arxiv.org/abs/1908.01300

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/