Nick Haynes

@bbstats op is not a good take but

this tweet lives another day

@bbstats Proud of you, son.

R

@bbstats new plot who dis

=O cc @NickDHaynes

@tublitzed Torties are the best!

Meet Isa, the cutest little kitten who joined our family this week ❤️

Listening to the most recent @strongerbysci fireside chat that talks about data analysis errors in Excel, and open up my computer this morning to find... 🤦 @GregNuckols theverge.com/2020/8/6/21355…

What makes production ML hard? - Cleaning, labeling, and augmenting data - Troubleshooting training and ensuring reproducibility - Deploying models and monitoring their real-world impact To help, we're excited to announce our online production ML course: course.fullstackdeeplearning.com

@AdamBSmith +ve for SMB: shorter sales cycle = you learn what works (with messaging, pricing, product, etc) and what doesn't faster

What am I missing?

Sales battle! SMB/Niche market vs Enterprise For this thread, SMB/Niche means a product that still requires getting on the phone with a customer, so a price point of $100-200+/mo.

Many people start attacking a problem by deploying the most sophisticated method possible with the belief that it will lead to the best results. I believe it might be better to chisel away at the complexity and find the simplest possible combination that expresses the key ideas.

@stanfordnlp Whoa whoa, chill. I agree with their approach - it's a great paper!

@NickDHaynes Umm … read the paper … they’re not using automatic metrics, because they rightly regard them as unsatisfactory for this purpose.

Important to remember that BLEU (or ROGUE, or other quantitative metrics) don't tell the whole story - two texts w/ same metrics can have very different perceived levels of quality to human readers. twitter.com/stanfordnlp/st…

Teams + Fans + Tech panel moderated by @YahooSports Michael Beshar looks at how new tech enables teams to build fan affinity & drive broader consumption of content @jaynebwise @nakanofrank #SMT18

Maybe progress in Vision/NLP/etc is not due to neural nets, but rather due to the influx of smart researchers, lots of compute, and industry pushes. Focusing on a different paradigm (graphical models, evolution, ..) could’ve made the same amount, but different type, of progress.

The fundamental irony of Twitter: the short format allows your audience to exist, but bc the audience exists, you want a longer format.

And it's hard to be concise with something that feels incredibly important to you.

So when you have something (seemingly) important to say, you want to say it to the largest possible audience.

The reason tweetstorms exist is that, for the vast majority of people, Twitter is the largest audience available to you.

GO PACK GO twitter.com/MartysaurusRex…

"as a general principle of learning, it 'somehow smells right.'" Well, I'm convinced. quantamagazine.org/new-theory-cra…

The belief that stacking layers deeper leads to better models is just a myth pushed by the big DL lobby to sell more tensors