InfoQ Homepage Algorithms Content on InfoQ
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The Quest for the One True Parser
Terence Parr shows the key practical advances in parsing from the last 25 years, provides algorithm comparisons, and separates the promises from reality.
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Not Exactly! Fast Queries via Approximation Algorithms
Fangjin Yang, creator of Druid, shows how approximation algorithms can help system scale out linearly and process huge amount of data quickly with small memory footprint.
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Machine Learning at Netflix Scale
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
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Order Notation in Practice
Roger Orr solves a problem with different levels of complexity trying to answer what the complexity notation actually means and why it is important in practice.
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Revealing the Uncommonly Common with Elasticsearch
Mark Harwood shows how anomaly detection algorithms can spot card fraud, incorrectly tagged movies and the UK's most unexpected hotspot for weapon possession.
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Top 10 Performance Gotchas in Scaling In-memory Algorithms
SriSatish Ambati shares tips for in-memory algorithms, discussing I/O, S3 resets, muxers, primitive byte arrays, non-blocking structures, and fork/join queues.
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Machine Learning & Recommender Systems at Netflix Scale
Xavier Amatriain discusses the machine learning algorithms and architecture behind Netflix' recommender systems, offline experiments and online A/B testing.
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Lock-Free Algorithms For Ultimate Performance
Martin Thompson discusses the need to measure what’s going on at the hardware level in order to be able to create high performing lock-free algorithms.
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The Algorithms Still Count
Shawn Wallace takes a look at several problems explaining how to evaluate possible solutions and to compare with each other.
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