I've had great luck with graduate students (and sometimes even ungrads): I'll find a grad student who knows the topic, often by searching for lesser known content creators on youtube/blogs, and reach out with an offer to pay for their time to explain a particular topic. So far it's worked out great, and I've created relationships with smart up and coming people in the field - they make meaningful money, I accelerate my learning. I ended up eventually hiring one, and another did consulting for me for some time. We had a routine where I'd ask them to read and summarize / teach me ML papers that were of interest to me, which they in turn could use in their studies/thesis/youtube channels.
Tips on this: content creators tend to be more open to and better at explaining things, and you get to see their ability to explain before you pay them. If you can, overpay them - students need the money more than you do :-)
This is very good advice. It works even better if the concept you are looking for is on the MS level rather than PhD candidate level because its pretty easy to beat T.A wages in most jurisdictions.
This is also a great way to do interviews if you have a small number of candidates: Just make them explain a final year BS / first year MS topic from the beginning without handwaving.
I wonder how you word your cold emails so that people bother to respond. Do you offer an hourly rate upfront in the first email? Thing is as someone who’s quite frequently on the receiving end of cold emails, I respond to companies looking for contracting/consulting because I know they probably have an at least passable rate in mind, whereas the few times I responded to individuals offering to pay for mentoring or feature development in my open source projects, after a few back and forth I realized they expected to pay one to two orders of magnitudes less than what I normally charge (e.g. $50 for about a week’s work, true story), it’s frankly embarrassing, so now I no longer respond to this sort of individual requests.
Two had small youtube channels delving into details of ML papers, one had a blog with a good explanation of a specific detail on a paper I was interested, one was a recommendation by my graduate advisor (I keep in touch with the university I graduated from a thousand years ago). The undergrad had a fantastic comment on a thread somewhere, I think in Kaggle.
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Join us in Coupa's (NASDAQ:COUP) data platform team to build and extend the core technologies used in analyzing customer data and providing insights. Spark and python for the backend, Django and Rails for apps and services, machine learning of all sorts (simple classifiers to deep learning). If you're a great engineer with experience or interest in data and machine learning and a good understanding of stats this would be a good match.
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> The rewards for working on new ideas are weighted by the value of the outcome. So it's worth working on something that only has a 10% chance of succeeding if it would make things more than 10x better
This is the primary point here - the "crazy idea" may (and likely does) have a low probability of being correct and succeeding, but with an outsized reward it may still be worth pursuing. This is, almost by definition, the correct way of looking at it.
Also note that he's talking about crazy ideas from "reasonable domain experts", not your run of the mill crazy.
This is not just a response to Mighty - he's been talking about this for years.
Coupa | San Diego or San Mateo / Bay area | Software Engineer, Data Platform Team | On-site with WFH days | Full Time
Join us in Coupa's (NASDAQ:COUP) data platform team to build and extend the core technologies used in analyzing customer data and providing insights. Spark and python for the backend, Django and Rails for apps and services, machine learning of all sorts (simple classifiers to deep learning). If you're a great engineer with experience or interest in data and machine learning and a good understanding of stats this would be a good match.
Coupa | San Diego, CA | Software Engineer, Data Platform Team | On-site with WFH days | Full Time
Join us in Coupa's (NASDAQ:COUP) data platform team to build and extend the core technologies used in analyzing customer data and providing insights. Spark and python for the backend, Django and Rails for apps and services, machine learning of all sorts (simple classifiers to deep learning). If you're a great engineer with experience or interest in data and machine learning and a good understanding of stats this would be a good match.
Most likely that will be very hard to do, at least for orchestral pieces, which are typically recorded using overhead microphones (ie, not with separate tracks for each instrument).
For pop music, it's definitely doable as long as I can get access to the individual tracks. It's actually something I have been wanting to do, so thanks for the suggestion.
On the Python side there's Lamson and Google App Engine also has nice email handling capabilities allowing you to parse and access from your own code / app.
Looks pretty good, I will check this out in more detail! It looks like a common enough problem that I thought there would be mature frameworks around. I keep finding myself looking for a solution like this for most of my side projects..
I had the exact opposite reaction - allowing me to play songs for free let me experience the service, so by the time they asked me to sign up I was happy to.
If I'd been able to listen for longer than 10 minutes so I could get a feel for how well the selected songs match with what I want to hear, I would be too. But they're not the only service out there offering free music, and besides, "free" isn't enough to entice me to do anything anymore. I get into my local movie theater for free because I'm friends with their their HR manager. I never use it.
Tips on this: content creators tend to be more open to and better at explaining things, and you get to see their ability to explain before you pay them. If you can, overpay them - students need the money more than you do :-)