AI Learning Guide
AI Learning Guide
This is less a curriculum than a pile of notes I keep nearby — the pieces I wish I’d had in one place when I was first getting a footing, and a few later ones I still go back to.
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AI Beginner's Guide: Learning Artificial Intelligence from Scratch
Artificial Intelligence (AI) is one of the most exciting technology fields today. This article will provide you with a comprehensive guide to getting started with AI.
Ranking and recommendation
Classic Foundational Papers on Ranking & Recommendation Systems
Ranking and recommendation systems power everything from Google Search to Netflix suggestions. While today’s systems use deep learning and large language models (LLMs), their foundations were laid decades...
Deep Learning Era of Ranking & Recommendation Systems: Must-Read Papers (2016–2020)
Explore how deep learning transformed ranking and recommendation systems, from Wide & Deep to BERT4Rec, covering core technologies powering platforms like YouTube, Facebook, and Amazon
Sequential Learning in Recommendation Systems: From Markov Chains to Transformers
A comprehensive guide to sequence-based recommendation techniques and key research papers, tracing the evolution from early statistical methods to modern transformer-based architectures
Contemporary RecSys: Industry-Scale Architectures & Multimodal Systems (2020–2025)
This era represents a pivotal shift toward production-ready, billion-scale architectures that power today’s major platforms. This comprehensive guide covers the essential papers that define contemporary RecSys, from industry-standard...
Modeling Long User Histories for Ads Ranking
How ads ranking systems went from aggregated feature counts to retrieve-and-compress architectures that handle 10,000+ user events under millisecond latency constraints.
Language models and reasoning
Reasoning in Large Language Models: A Research-Centric Overview
“Can LLMs reason?” is one of those questions that generates more heat than light, mostly because people mean very different things by “reasoning.” This post tries to cut...
Training signals, reward, and alignment
The Evolution of Reward Hacking and Jailbreak Research in AI
From specification gaming in classical RL to deceptive alignment and jailbreaks in LLMs—a survey of how reward hacking has become a central challenge in AI safety and alignment....
Why Final-Outcome Rewards Are Not Enough for AI Agents
An outcome reward tells you whether a trajectory worked. It doesn't say why. I think the interesting question is whether the process rewards we train on today measure...
More to come.
Adjacent: Paper Readings · Research Blog