Codetown ::: a software developer's community
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
Added by Carol McDonald on August 4, 2015 at 11:15am — No Comments
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,…
ContinueAdded by Carol McDonald on April 13, 2015 at 9:14am — 1 Comment
Added by Carol McDonald on March 30, 2009 at 10:30am — No Comments
Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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AWS previews Route 53 Global Resolver, using Anycast to decouple DNS from regional failures. It simplifies hybrid setups with unified public/private resolution, DoH/DoT, and Zero-Trust security.
By Steef-Jan Wiggers
In this article author Sachin Joglekar discusses the transformation of CLI terminals becoming agentic where developers can state goals while the AI agents plan, call tools, iterate, ask for approval where needed, and execute the requests. He also explains the planning styles for three different CLI tools: Gemini, Claude, and Auto-GPT.
By Sachin Joglekar
Conducted among over 1,200 respondents, Facebook's 2025 Typed Python Survey highlights how and why Python developers have increasingly adopted the language's type hinting system. The survey also sheds light on what developers value most, as well as their biggest frustrations and wishes.
By Sergio De Simone
Alex Seaton discusses the architecture of ArcticDB, a high-performance Python/C++ library that replaces traditional database servers with a thick-client model. He explains how to achieve atomicity on object storage through bottom-up writes and shares deep insights into conflict-free replicated data types (CRDTs). He also explores the pitfalls of clock drift and distributed locking.
By Alex Seaton
Meta applies large language models to mutation testing through its Automated Compliance Hardening system, generating targeted mutants and tests to improve compliance coverage, reduce overhead, and detect privacy and safety risks. The approach supports scalable, LLM-driven test generation and continuous compliance across Meta’s platforms.
By Leela Kumili
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