From Research Paper to Running Code
Exploring how AI can dramatically accelerate the process of turning complex academic research into functional code, with examples from anomaly detection to small LLMs.
Advanced Anomaly Detection
Deep dive into Robust Random Cut Forest (RRCF) implementation for real-time anomaly detection in Application Security Platforms. Learn how advanced machine learning algorithms enhance threat detection and automated response capabilities.
Double MAD?
This article explores the use of Double Median Absolute Deviation (Double MAD) for anomaly detection in time series data, particularly in skewed or non-symmetric distributions.
Double MAD vs the Rest
A look at the limitations of Double MAD for anomaly detection, and a comparison with the Z-score method, to help you choose the right approach for your data.
Scaling anomaly detection with RRCF
Discusses strategies for scaling the Robust Random Cut Forest (RRCF) algorithm for large-scale anomaly detection, including using summary statistics, buffering input, and parallelisation.
Applied RRCF - thresholding techniques.
Explores various thresholding techniques like Median Absolute Deviation (MAD), Min/Max, and Z-Score for interpreting Robust Random Cut Forest (RRCF) anomaly scores, crucial for classifying data points as normal or anomalous.