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 the functional components of th...

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...

Let’s understand BPTT algorithm in RNN [S_t = \sigma(U.x_t + W.S_{t-1} + b)] [y_t = O(V.S_t + c)] Derivation of Gradients in RNNs 1. Gradient Flow [\frac{\partial L_t(\theta)}{\partial W} = ...

Regularization in deep learning is a set of strategies used to prevent a model from overfitting. In simple terms, deep neural networks are “data hungry” and highly complex. Without constraints, th...

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...

In the architecture of a deep neural network, activation functions are the crucial component that breathes life into the model. Without them, even the deepest network would be nothing more than a g...