Not a complete answer to what you're looking for, but you might find this dive into PyTorch internals interesting: http://blog.ezyang.com/2019/05/pytorch-internals/
This is actually very close to the flavor I had in mind.
Readit Newsmonadmancer commented on Practical Deep Learning for Coders course.fast.ai/index.html... · Posted by u/samrohn
monadmancer commented on Practical Deep Learning for Coders course.fast.ai/index.html... · Posted by u/samrohn
If you want to take a look at a framework that isn't a terrible mess and that one person can understand both Darknet [1] and Flux [2] are really nice. Learned a lot from reading their source.
These are refreshing. Redmon's resume is, the first of its kind I suspect.
monadmancer commented on Practical Deep Learning for Coders course.fast.ai/index.html... · Posted by u/samrohn
The lessons look interesting from a high level perspective. And I think could help people guide their applications.
I think there's also a need for a very low level course in deep learning. I.e. on the level of someone who wishes to write their own deep learning library. Because from high up, sure it all looks like the chain rule, but down low, it gets messy quickly if you want to write a high performance library on your own.
monadmancer commented on Data Science Is America’s Hottest Job bloomberg.com/news/articl... · Posted by u/dsgerard
The field is absolutely saturated with people who want to be a data scientist but have no experience. This is where some of that gate keeping comes from.
What are the data science "gotchas?" A lot of people can pick up basic programming in a weekend, but they wouldn't necessarily know what they don't know and might well get deeply mired in problems with concurrency or algorithmic complexity.
It's such "gotchas" which justify gatekeeping. Otherwise, gatekeeping is just unproductive manipulation of the market.
I think the obvious "gotchas" are problem definition (Am I formulating the problem in a way that will allow me to create value? a concrete example: am I modeling churn correctly?), overfitting, target leaks, and model trouble shooting / improvement (i.e. the model is doing OK, can it do better? How much better? How do we get there? Remembering that small performance gains can mean big $ at scale). On the reporting side, how confident that what I'm reporting is real? This is where the "science" training is helpful. Programming experience is relevant in the sense that implementation is important, i.e. it's far too easy to introduce critical target leak bugs when engineering features.
Of course we can abstract the root argument; for a given job, among those qualified to fill that job, there exists at least one person who has auto-learned the skills required to perform the job. This is probably true.
monadmancer commented on Data Science Is America’s Hottest Job bloomberg.com/news/articl... · Posted by u/dsgerard
The White Walkers of the data science field are out the box enterprise solutions. These are enterprise software, data science consulting and ops solutions in a package. The corporate customer need not hold in house a data science team. The ambitions of the enterprise solutions (think DataRobot, H2o, etc) are to effectively bring one click production ready solutions that even C-level can participate in.
I see this is as the greatest threat to the demand of the "in house" data scientist.
If this turns out to be the case, I see the greatest demand for those who can write production grade code (i.e. software engineers) and those who are effectively trained data scientists. We see this job often called research scientist or research engineer.
monadmancer commented on A daredevil climber risking his life for breathtaking views huckmagazine.com/places/v... · Posted by u/bspn
I think the next logical step is to base jump from these towers.
>as more and more money is transferred to the people
Where do you think this money came from in the first place? You think the value of bitcoin was driven up by the 1%? Or magically created by gnomes like fractional reserve lending?
I believe "to the people" is another way of saying not directly to large software corporations. To the people from the people.
> products that people actually want
This part of the process is usually considered one of the first steps in a successful business.
If we're talking about developers, then there are already a variety of projects that "people" developers actually want.
The consumer oriented blockchain-natively-dependent projects are not here yet because the protocol layer isn't mature enough.
This isn't just my opinion, but those of the "actual" investors in the blockchain space such as a16z and polychain.
> Imagine you’re investing in Apple in 1980. There is plenty of risk involved and you’re still not sure this Jobs kid has what it takes, but do you like the idea? The mission? The product?
This is delusional. From Apple's Wikipedia:
"Between September 1977 and September 1980, yearly sales grew from $775,000 to $118 million, an average annual growth rate of 533%"
They were selling products that people wanted for profit. Comparing ICOs to this is ludicrous and seems to me to encapsulate the hazy thinking of this entire sector.
We aren't there yet. Successful projects are building protocols with the goal of developers using them to build next-gen products that people actually want. Current speculation (a16z, polychain, ...) is that it will take another few years for this rollout to really take effect.
monadmancer commented on A LessWrong Crypto Autopsy lesserwrong.com/posts/Maj... · Posted by u/henryaj
I was logically onboard back then, but the notion of Proof of Work mining seemed ridiculous and wasteful, so I didn't bother. I didn't even consider its purchase, which in retrospect would've been a good idea?
I'm happy to hear the field is moving to Proof of Stake. I would've been on board from the beginning most likely.