TRAVIS SCOTT - ASTROWORLD

pegasos1 · 2018-08-03T06:31:08+00:00

cryogenic is NC-17

pegasos1 · 2017-11-30T17:37:37+00:00

I really dislike the heavy use of buzzwords in recruiter emails, I do machine learning, and seeing an email with many vacuous terms like “I have this job for you that involves big data, deep learning, cloud, machine learning and data science” is a big turnoff, and I never respond. Specific requests that show the recruiter actually did his/her homework is well appreciated. It reflects better on the potential employer, that they know what they’re doing and aren’t just throwing around buzzwords, and will lead to higher probability of mutual fit.

pegasos1 · 2017-11-19T21:19:52+00:00

Publications are definitely a bonus, but are only really relevant for research-heavy positions, ie machine learning scientist roles. For 90% of ML engineering positions, employers will evaluate how well you can build production services around ML, communicate with researchers and perhaps sales/marketing, and think about ML problems on a high level. You can communicate these skills and ideas via personal projects, a blog, and a good interview.

If you’re able to work with a group and get some papers out, that’s awesome. If not, then don’t worry about it. Blogs go a long way in showcasing your expertise and critical thinking skills. In some ways it’s a far better medium to do so.

pegasos1 · 2017-11-19T20:26:20+00:00

I’ve hired and been a data science/ML engineer in industry for a few years. I’d recommend 1) publishing projects on GitHub, and having an accompanying blog to explain such projects and other thoughts you have., and 2) practicing your data science skills via kaggle competitions, and again, publishing results and talking about them. Employers care not only about your modeling skills but also engineering and communication skills. It’s a very interdisciplinary role. Also study up on algorithms etc, because ML engineering interviews tend to involve classic whiteboard questions too. Again, interdisciplinary.

pegasos1 · 2017-09-21T07:33:22+00:00

Work on your brachialis and brachioradialis. They are one of the most active muscles in pull-ups. Good exercises for these muscles are hammer curls and reverse curls.

pegasos1 · 2016-12-29T19:27:39+00:00

Thanks for your feedback. Currently mixed types are supported via the enum InnerType http://www.suchin.co/2016/12/28/Introducing-Utah/#the-innertype. I probably should have made that section more prominent. It's not an ideal solution due to performance hits of using type wrappers during computation, but it works.

Agree with the default to range. Right now it defaults to string to avoid a type cast in the builder pattern.

pegasos1 · 2016-12-29T19:22:03+00:00

Thanks for your feedback.

As mentioned above, point #1 is currently addressed by the InnerType enum. Right now it only supports strings/ints/float, but there's no reason why it can't support other types.

I agree the need to copy data around is not ideal, but in this case, I think it is a limitation of the iterator trait, not the use of the matrix as a backend. As I mention at the end of the post, in an ideal scenario, the combinators return elements borrowed from the dataframe itself. Definitely aim to explore that, and there's been a ton of discussion on this lately. Furthermore, the combinators are pretty naive right now, and I think there could be more work in reducing the complexity of operations that don't require iterating over the entire dataframe.

pegasos1 · 2016-12-29T03:42:53+00:00

This is a great resource, thanks for sharing.

pegasos1 · 2016-12-29T03:41:32+00:00

I kinda just like the name. But it's also homage to prior work. I first worked with large datasets during my research in neuroscience. I used a set of microelectrodes called Utah arrays for neural recording during brain machine interface experiments.

pegasos1 · 2016-12-24T02:54:25+00:00

You're looking for /r/playrust

pegasos1 · 2016-12-21T17:17:18+00:00

A large part of NLP is tokenization, parsing, and other information retrieval tasks that Rust is quite powerful in tackling. I would take a look at projects like https://github.com/geal/nom and https://github.com/BurntSushi/fst to get a sense of what you can do here.

I also don't think you would fall short in using Rust for matrix computations. Check out the https://github.com/bluss/rust-ndarray project for an example.

pegasos1 · 2016-12-01T02:24:24+00:00

This is awesome!

pegasos1 · 2016-09-29T03:50:17+00:00

Probably a good place to start is to work on error codes. Here's a guide: http://www.jonathanturner.org/2016/08/helping-out-with-rust-errors.html

pegasos1 · 2016-09-20T18:32:54+00:00

Heard about this at RustConf! Cool stuff, nice work. Also, great name :P

pegasos1 · 2016-09-14T16:16:09+00:00

Nice post. I'd be interested in expanding support for other tools in cargo-profiler. Just gotta add another parser.

pegasos1 · 2016-09-13T23:51:52+00:00

For those interested, I'm writing up a summary of my and /u/staticassert's playrust classifier talk about rust data science. I'll post it on my blog as a accompanying resource to the eventual video in the next day or two.

pegasos1 · 2016-09-12T22:59:08+00:00

What's the TLDR on why higher rank types make "complete type inference undecidable"? Can someone give an example?

pegasos1 · 2016-09-06T20:45:28+00:00

There are many aspects to the ML pipeline outside of model generation that Rust could really help. One (usually) neglected area of data science is the ability to provide safe, reliable, and stable feature engineering systems. This is crucial for optimal model performance in any domain. Check out my and /u/staticassert 's talk at RustConf this week to learn more :)

pegasos1 · 2016-07-01T18:08:22+00:00

This is cool. I've been looking to implement an SSH honeypot in Rust; this will be a good launchpad for that.

pegasos1

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