Coding, waiting for results, interpreting them, returning back to coding. Plus, some intermediate presentations of oneβs progress. But, things mostly being the same does not mean that thereβs nothing ...
Similar Articles (10 found)
π 68.7% similar
A couple weeks ago I went heads-down and experimented with a new development model. The results were unexpected: a production-ready application, ~800 ...
π 66.6% similar
Anyone Else Struggling to Keep Up With Data Tools
You canβt outlearn the internet, but you can learn what matters
Hi, fellow future and current Data L...
π 66.0% similar
Does the Bitter Lesson Have Limits?
Recently, βthe bitter lessonβ is having a moment. Coined in an essay by Rich Sutton, the bitter lesson is that, βg...
π 65.2% similar
Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editorsβ picks, deep dives, community news, and mor...
π 65.2% similar
> the generation of 281,128 augmented examples, from which 1,000 were
held out as a benchmark test set.
This model is trained on a custom dataset of 2...
π 64.7% similar
Intro
I was listening, recently, to an episode of The Pragmatic Engineer podcast with Armin Ronacher, and something he said really resonated with me. ...
π 64.6% similar
5 Things in Data Engineering That Still Hold True After 10 Years
Why core challenges in data engineering resist the test of time
Hi, fellow future and...
π 64.4% similar
First, thanks to the publisher and authors for making this freely available!
I retired recently after using neural networks since the 1980s. I still s...
π 64.4% similar
What Separates Good From Great Data Teams
A guide to shifting from outputs to outcomes in your data career
Hi, fellow future and current Data Leaders;...
π 63.9% similar
A Recipe for Training Neural Networks
Some few weeks ago I posted a tweet on βthe most common neural net mistakesβ, listing a few common gotchas relat...