99decisionstr

4 ms·

1. 1.▲
   by 99decisionstr 11y ago

   Sure! Here are some insights: https://news.ycombinator.com/item?id=10136197 D is awesome for what we do with it. It unlocks our small team of data scientists to have a huge impact on the company, as we don't have to re
2. 2.▲
   by 99decisionstr 11y ago

   In very large-scale sparse settings, the optimization strategy is tightly coupled to the modeling, not orthogonal to it. The reason why is that "data beats algorithm": meaning that a "dumb" model that can be trained on 1
3. 3.▲
   by 99decisionstr 11y ago

   The idea is that FM is a way to learn the polynomial kernel, by representing each high-order term as a low dimensional dot-product. It improves generalization error by learning a better polynomial kernel, because terms in the kernel are l
4. 4.▲
   by 99decisionstr 11y ago

   Not quite - it's different from the kernel trick, which is impossible at this scale (there's no way you can train an RBF kernel in a decent amount of time when your space has 10^8 features and your training set has 10^9 observatio
5. 5.▲
   by 99decisionstr 11y ago

   We built all of our machine learning backend in D at AdRoll. Some examples: - learning of large-scale classifiers and regressors using custom optimizers - real-time pricing of billions of ads a day using these models on ad exchanges. <.5