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Posted on August 4, 2015 at 11:15am 0 Comments 1 Like
This post discusses building a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.
https://dzone.com/links/parallel-and-iterative-processing-for-machine-lear.html
Posted on April 13, 2015 at 9:14am 1 Comment 0 Likes
Recommendation engines help narrow your choices to those that best meet your particular needs. In this post, we’re going to take a closer look at how all the different components of a recommendation engine work together. We’re going to use collaborative filtering on movie ratings data to recommend movies. The key components are a collaborative filtering algorithm in Apache Mahout to build and train a machine learning model,…
ContinuePosted on March 30, 2009 at 10:30am 0 Comments 0 Likes
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Storybook, the front-end workshop for UI development, has officially released version 9, bringing improvements to testing through a collaboration with Vitest and other core upgrades such as a flatter dependency structure to optimize performance and improve the overall developer experience.
By Daniel CurtisAnna Berenberg talks about One Network, Google's unified service networking overlay, which centralizes policy, simplifies operations across diverse environments, and enhances developer velocity. Learn about its open-source foundation, global traffic management, and vision for future multi-cloud and mobile integration.
By Anna BerenbergOla Hast and Asgaut Mjølne Söderbom gave a talk about continuous delivery with pair programming at QCon London. Their team uses pair and mob programming with TDD; there are no solo tasks or separate code reviews. This approach boosts code quality, reduces waste, and enables the sharing of knowledge. Frequent breaks help to maintain focus and flow.
By Ben LindersMeta has begun rewriting its mobile messaging infrastructure in Rust, gradually replacing a legacy C codebase that engineers say had become increasingly difficult to maintain and frustrating to work with.
By Matt FosterThe panelists demystify AI agents and LLMs. They define agentic AI, detail architectural components, and share real-world use cases and limitations. The panel explores how AI transforms the SDLC, addresses concerns about accuracy and bias, and discusses the Model Context Protocol (MCP) and future predictions for AI's impact.
By Govind Kamtamneni, Hien Luu, Karthik Ramgopal, Srini Penchikala
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