Hi Friends working in industry as deep/machine learning engineers,

Are there any benefits on having an extensive knowledge of optimization algorithms beyond the variations of gradient descent? (i.e. Chambolle-Pock, Split Bregman, Proximal methods, etc.)

I’m debating on spending my next semester taking a course either on this subject or deep learning for computer vision. I’d love to hear everyone’s opinion on which skills would be more relevant for industry.

Many thanks!

Hey! Some buddies and I are starting an organization for AI research collaboration, and we’re looking for members. We currently have six figures of funding and several deep learning experts on our team. Our goal is to work together to do what we otherwise couldn’t alone, and produce research/products that are valuable to a lot of people. We can provide technical and moral support as well as huge amounts of TPUv3 compute for research projects.

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If you’re interested, shoot me a message with your background/experience as well as what you’re interested in working on. We’re open to all ideas, but we’re especially interested in research directions that can benefit society. There are no qualifications; we’re just looking for people who are passionate about AI and are willing to put in work to publish research and build technology that is valuable to the world. We’re also looking for co-founders and mentors!

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We look forward to hearing from all of you!

So when making a deep recommender system, I see an obvious problem with making it work with a NN, versus some kind of correlation analysis. Basically when you have a network doing the recommending, whenever you add new things to recommend (new products, movies, books, etc) you’ll have to retrain the model on the new “products” to get recommendations including those new things.

Is there an easy way to structure a network’s architecture to mitigate this problem? Or do you just have to retrain the model all the time?

Thanks!

In the words of my 9 year old niece, the dab is “well.. considered old fashioned.”

So, here’s a completely ridiculous project, lights controlled by old fashioned dance moves.

To build training data I reused the OpenPose Python example, adding a keypress to build an array of sample data for either category of dab, tpose, or other.

Once I got that data, I went through it interactively in a Jupyter Notebook, where I cleaned it up and then trained a network on 171 sample poses.

With that trained network, I then used it along with a Z-Wave Z Stick on my NVIDIA Jetson, and controlled the lights on my network with this program I wrote glueing everything together.

All the code is on Github.

If you’re interested, I also wrote a blog post with more visuals.

Without having to download the whole data-set.

For example, I’m interested in progress in NLP. Recently machine performance has exceeded baseline human performance on the MS Marco Q&A task. It’s hard to have a real sense of what this means without downloading the whole evaluation portion of the MS Marco data-set, which I don’t particularly want to do. If you’re going to go to the trouble of putting up a leader-board, you might as well include a page with a sample of a hundred questions or so.

Hats off to people who provide plentiful examples of the kind of questions in their data-sets including SQuAD, The Winograd Schema, the Ai2 people, ReCoRD and many others.

I want to train an MLP to multiply two inputs. The easiest approach is to generate a vast dataset of numbers and their multiplication and train the network on them.

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I did this as a test and the MLP worked nearly flawless and long as I gave it numbers inside the training range but as soon as I deviated from the training domain the network started malfunctioning.

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I wanted to know if there is an architecture which can learn the semantics of multiplication and act properly outside its training domain.

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Thanks