Anomaly Detection Belongs in Your Database — built SIMD-accelerated isolation fo

<!-- SC_OFF --><div class="md"><p>Disclosure: I built LabelSets (labelsets.ai). Sharing the technical approach behind how we score dataset quality.</p> <p>THE PROBLEM</p> <p>Most

<!-- SC_OFF --><div class="md"><p><a href="https://www.reddit.com/r/MachineLearning/?f=flair_name%3A%22Project%22"></a>Hey everyone. I’ve been working on a machine learning

<!-- SC_OFF --><div class="md"><p>I am currently using the dataset CICIDS2017 to train an Isolation Forest model for anomaly detection, I am currently using a split that consists

<!-- SC_OFF --><div class="md"><p>what the title says. this is a pretty big paper in the deep learning anomaly detection space, accepted at the International Conference on

<!-- SC_OFF --><div class="md"><p>Hey everyone!</p> <p>I’m excited to announce the release of <strong><code>py-evoFE</code></strong> (v0.3.0) — an open-source Python library that

<!-- SC_OFF --><div class="md"><p>Just a Student trying to build some projects and this one caught my attention.</p> <p>An automated system for detecting statistically significant

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s2e0d5/built_an_opensource_llm_router_for_consumer_gpus/"> <img alt="Built an open-source LLM router for

<!-- SC_OFF --><div class="md"><p>I've been building memFrame — an open-source dataframe API that compiles operations to SQL.</p> <p>The idea:</p> <p>**Python/DataFrame API → SQL