I’ve trained a tensorflow model which takes my RTX2080 several seconds per action (in addition to 20-30 seconds to initialize the model). I’ve been looking into turning this into an iOS/Andriod app running on tensorflow lite, but apart from the technical challenge of converting the model into a tensorflow lite model and everything else, am wondering about the feasibility of this running on phone hardware – even on a reasonably modern phone with inbuilt GPU would this still likely be too slow for practical purposes? Can anyone who has built an iOS/Android app with tensorflow lite where the phone is responsible for computation comment on performance and other practical considerations? The only other option of having requests served by my own server(s) on AWS for example would turn into a major expense if the app had significant use.

I have been working in machine learning professionally for a little over 3 years now, 2 of which were spent working as a freelancer through Upwork.com and 1 of which was spent as the founder of a start up company. However, I’ve been considering moving into a more traditional role at a firm mainly to gain the experience and be able to learn from other people at the company. Not to mention freelancing can get pretty lonely over extended periods of time. I didn’t finish my degree yet, however I only need my capstone and ~9 credit hours to complete it, and right now I am just doing the Senior Capstone and am not sure I am going to pursue completion (I know it sounds stupid, just trust I’ve given it a lot of thought, and I am not here to discuss that). Also, my Upwork profile is in great standing and has 5 star reviews for large long-term projects and a 100% job success rate.

Anyways, I ended being contacted by a recruiter on LinkedIn, submitted my resume, and now I have my first interview in the field on Friday. I am not so much nervous as I am just not sure what to expect, and I was hoping that people who have experience with machine learning interviews might be able to give me some pointers and tips, let me know anything I should review and make sure to know, etc. As far as any coding that they may require, I feel fairly confident about being able to solve ML problems effectively. But I don’t know whether they do the standard coding interviews for these roles, in which case I need to brush on my Data Structures and Algortihms for sure.

And lastly, I thought about asking the person who’s going to be interviewing me what to expect, but then decided that doing that is unprofessional and might make look bad or something (I hate fuckin’ corporate politics, but that’s OK, I can deal for awhile). Was this the right decision or is it normal to ask something like that?

Many thanks again guys, and I think it’d be great if this post could just serve as a sort of repository of advice for interviewing for ML jobs in general, so when offering advice I think it’s best to think what’s the best advice I could in general and pretty much ignoring my specific concerns. Cheers!

Hey there, I am a Master Student from germany currently working on some pose detection with tf pose. I am looking for a tool which can generate realistic 3d pose keypoints. It does not need create images for Training, just realistic 3d keypoints for customizable poses. Has anybody of you already experience with such a “Tool”? I would be very grateful for your help. Thanks

These are the top 3 most popular talks (by views) from MLconf NYC 2019. All 3 are excellent talks: The next MLconf event is in San Francisco on 11/8, that’s next week!

1. Rishabh Mehrotra, Research Scientist, Spotify: Personalizing Explainable Recommendations with Multi-objective Contextual Bandits
2. Emily Pitler, Staff Research Scientist, Google AI: Representations from natural language data: successes and challenges
3. Nitin Sharma, Research Scientist, PayPal: Deep Learning Applications to Online Payment Fraud Detection

*If you’d like to attend MLconf in San Francisco next, you can use the discount code “slashml19” for 50% off, register here: https://www.eventbrite.com/e/mlconf-sf-2019-tickets-52641374769 – This code is only good for 6 total tickets, once it’s out it will say it’s expired. First to register gets the discount.

You can find all of the past MLconf videos on the MLconf YouTube page here. Subscribe to see the newest talks as they arrive.

Please let us know what you think of the talks, speakers, topics, and anything you would be interested in for future events here in the discussion.

For better or worse, OpenReview gives a lot of power to individuals not assigned reviewer roles. We’ve all seen good examples of this (e.g. pointing out something important you’re sure could be missed by a reviewer, like “your code uses train data at test time!”) and bad examples of this (e.g. trolling, or pure opinions like “This is a useless result.”) But there’s a lot room between these.

People who comment on OpenReview before paper-acceptances are finalized, and people with opinions on the matter, how should we as a community approach this new tool? What do you think you should be trying to add with a comment? What are you careful not to do?

https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning

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Deepmind releases AlphaStar and their soon-to-be-published Nature paper

I’m looking to clean-up a few of my Github repos and am wondering if anyone has good examples of Readme.md files they’ve seen/made, specifically for Machine Learning and/or Deep Learning projects.

Myself and a small team have been trying to improve unsupervised learning pipelines locally at the office and have realized that the solutions cross industry are pretty disparate and may represent a cool open source product oppurtunity. I’d love to get feedback from this group on where the biggest issues are for you. We put together a survey which goes a little more in depth,

Survey Monkey Link: https://www.surveymonkey.com/r/MLsurvey12

Thanks a ton for any feedback