In the previous posts, we explored how an ad impression is auctioned in milliseconds and how machine learning models decide whether and how much to bid. However, every system we’ve discussed - from...
In the previous posts, we explored the plumbing of AdTech: how ad tags, redirects, pixels, and browsers coordinate to make a single impression possible. But for a data scientist or engineer, the mo...
In the previous posts, we explored who participates in the AdTech ecosystem and how the supply and demand stacks are structured. In this post, we shift focus to how those systems actually talk to e...
In our first post, we looked at the high-speed “Invisible Auction” that happens every time a webpage loads. In this post, we’ll move from the marketplace overview to a single question that turns ou...
Welcome to the first part of our AdTech primer. For most developers, modern advertising technology is a black box of invisible network requests. In this post, we’re cracking it open to understand t...
How do we represent the meaning of a word to a computer? This is the fundamental question of Natural Language Processing. Simply treating words as unique IDs fails to capture the relationships betw...
Recurrent networks process a sequence one step at a time, reusing the same weights at every step, which is what “recurrence” means. That weight sharing is what lets them handle variable-length sequ...
Regularization in deep learning is a set of strategies used to improve a model’s performance on data it was not trained on, that is, to reduce overfitting. Overfitting is best understood as a gap:...
Optimization and stabilization techniques for DNNs focus on training stability (making sure the model actually learns without diverging) and optimization efficiency (reaching the best solution fast...
1. High-Level Overview: How Deep Learning Models Learn At its core, a deep learning model is a function approximation machine. It tries to map inputs to outputs by adjusting its internal parameter...