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...
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 s...
How Bigtable's lexicographic row keys drive every schema decision, and how Spanner delivers strongly consistent global SQL without escaping CAP.
A decision framework for picking data stores across an ML system, from training data in object storage to online features under a p99 budget.
When we ask an LLM a question, it doesn’t just “know” the answer. At each step it produces a score for every possible next token, one step at a time. But how it turns those scores into an actual ch...
Boosting series: Part 1: AdaBoost and Gradient Boosting · Part 2: The XGBoost Mathematics · Part 3: Choosing a Library Part 1 and Part 2 covered the algorithms and the XGBoost mathematics. This f...