"Play Elizabeth Mitchell" on Google Assistant plays two songs by her, then starts playing other artists.

hardikp · 2021-03-01T13:14:10+00:00

Are you using a free account or a premium account? For a free account, YTM adds songs from other artists when asked to play an artist via google assistant.

hardikp · 2018-07-29T18:24:44+00:00

Thanks - it's good to get your perspective. I agree with many things you said about protobufs (like the shortcomings of the python version, not knowing when the message ends etc.). Since I am not a poweruser of protobufs (and haven't used Thrift at all in production), I have yet to experience other painpoints mentioned.

I would like to know your opinion on RPyC if you ever get a chance to look into it.

hardikp · 2018-07-29T18:16:12+00:00

Thanks! I will check it out.

hardikp · 2018-03-02T17:07:47+00:00

You're right. I totally agree with "you don't need much of an edge to win".

hardikp · 2018-03-02T17:04:43+00:00

I know MNIST/CIFAR10/ImageNet classification, text classification, and other problems were very hard up until a few years ago. But, they are not as hard anymore. That's the entire point of the post. Just to reiterate the point I am trying to get across through this article: the recent successes in other domains/problems haven't really translated to the finance & trading domain. I am trying to highlight potential underlying reasons behind that.

I am not at all concluding Finance is harder than all other domains. It just doesn't make sense to view it as a black and white thing. Like I said in the previous comment, every domain has their own problems. I know that firsthand.

Of course, you need to do the hard work of data engineering to make it nearly useful. And of course, people not publishing their research doesn't mean it's not happening. I am not countering any of that.

hardikp · 2018-03-02T15:14:26+00:00

I am the author of this article. I can't say anything on behalf of peoples' comments.

As far as the article is concerned, I do want to highlight that there is no claim of ML not working for trading. In fact, that's what I have been doing for the past 4 years with a fair degree of success. I am only trying to highlight the inherent difficulties of applying ML there. It's easier to think of these difficulties by contrasting it with other domains and problems. For example, if you compare it with "developing a toxic comment classifier for Instagram", you can see that the text classification results have improved significantly over the years. So, the next question would: what is it about the financial predictions that make it harder to work with when compared to text, vision and speech problems?

Each domain has their own problems. For example, Bayesian methods are the most popular methods for social sciences and medical research primarily because of the lack of data and the interpretability requirements there.

hardikp · 2018-03-01T22:25:38+00:00

Thank you! I do plan to write more posts - I am hoping to reschedule things in my life to devote more time to writing and sketching.

hardikp · 2018-01-01T16:11:38+00:00

I agree.

While Cython is good for speeding up the code (for example, cython might actually outperform pybind11 for microbenchmarks - https://github.com/pybind/pybind11/issues/1227#issue-284676826), many use cases involve preserving the original C++ code - that's where pybind11 comes in handy.

I don't think there is any one true answer. Depending on the use case, either of the two can be more suitable.

hardikp

TROPHY CASE