My pet peeve is that the IPCC reports are balled up in these massive PDFs. So I exploded the most recent one into an Airtable, with sections linked from the summary and so on. Still some cleanup, but it's mostly there. Interested if this is useful for anyone.
That's fair pushback re how "standard" deep learning is. That said, those methods are rapidly establishing themselves as the go-to for applications in speech, vision, and text. At least for those outfits that can afford the substantial dev costs.
This is what I am hoping to launch with the launch of skymind[1]. I believe in the potential of deep learning, especially in the ways deep learning can learn better representations for feature extraction than some hand curated models.
Practicality has always been a problem with deep learning, and I think enabling access to this powerful technology will be a great enabler for many people in the long term.
Let me clarify: Techcrunch articles talking about papers being published.
Blogspam to me is just something that talks about the stuff at such a high level, there's no meat in it. Within that subset I'm talking about academic concepts where you can learn a lot more if you just read the papers.
Before I get bombarded here, I think it's best to clarify that the word blogspam is a term thrown around a lot around here.
When the press talks about an academic paper, they tend not to add much value in terms of actually explaining what's going on. I think people drawing their own conclusions from the original works is the better way to go. Obviously not everyone will agree with me here, but so be it.
Prior Knowledge (SF), Full Time, Distributed Systems Developers and Machine Learning Experts
P(K) is a small team in downtown San Francisco pushing the frontiers of probabilistic machine learning to the masses. Our first product is Veritable, a predictive database. We like to think of it as a database for things you don't (yet) know.
We're passionate about discovering the hidden causes behind data, and are currently split pretty evenly between machine learning experts, scalable systems engineers, and people used to working with horribly messy, complex, and sparse data. We're backed by Peter Thiel's grou, Founders Fund, who share our commitment to long-term technical innovation.
http://priorknowledge.com/join-us describes a bit more about what we're looking for, and you can always e-mail me (jonas@priorknowledge.com) for more info! Or swing by our offices at the intersection of 3rd and Market to meet the team.
Prior Knowledge (SF), Full Time, Distributed Systems Developers and Machine Learning Experts
P(K) is a small team in downtown San Francisco pushing the frontiers of probabilistic machine learning to the masses. Our first product is Veritable, a predictive database. We like to think of it as a database for things you don't (yet) know.
We're passionate about discovering the hidden causes behind data, and are currently split pretty evenly between machine learning experts, scalable systems engineers, and people used to working with horribly messy, complex, and sparse data. We're backed by Peter Thiel's grou, Founders Fund, who share our commitment to long-term technical innovation.
http://priorknowledge.com/join-us describes a bit more about what we're looking for, and you can always e-mail me (jonas@priorknowledge.com) for more info! Or swing by our offices at the intersection of 3rd and Market to meet the team.
