The Hidden Engine of AI — Training Frameworks and Resilience
A reader-friendly guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to checkpoints, mixed precision, and fault tolerance.
Tag: machine-learning
All the articles with the tag "machine-learning".
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The Hidden Engine of AI — Datasets and Dataloaders
A deep dive into how datasets and dataloaders power modern AI—from the quiet pipeline that feeds models to the sophisticated tools that make training efficient. Understanding the hidden engine that keeps AI systems running.
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XGBoost — The Art of Boosting Trees with Second-Order Gradients
A deep dive into XGBoost — how second-order Taylor approximations and sophisticated regularization make it the dominant algorithm for structured data, bridging mathematical rigor with system engineering excellence.
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The Transformer — How Machines Pay Attention
An intuitive introduction to the Transformer architecture — from the attention mechanism to self-attention and cross-attention, using language translation as a concrete example.
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Variational Autoencoders — From Compression to Creation
An intuitive introduction to Variational Autoencoders — how compressing data into probabilistic codes enables machines to generate realistic images, sounds, and structures.
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The Role of Predictions in Closed-Loop Autonomous Driving
Reflections on building production-grade behavior prediction systems at Zoox and Qualcomm — and why closed-loop reasoning is the bridge between perception and planning.