July 2025

Unsupervised, generalizable method for doing anomaly detection An ensemble of models, weighted according to their reluctance to flag anomalies, outperforms its predecessors. Machine learning Abishek Sankararaman July 15, 12:17 PM July 15, 12:17 PM In many of todays industrial and online applications, identifying anomalies rare, unexpected events in real-time data streams is essential. Anomalies can indicate manufacturing defects, system failures, security breaches, or other significant events. The typical machine-learning-based anomaly detection system is trained in a supervised manner, […]

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In this blog, you will explore hierarchical clustering in Python, understand its application in machine learning, and review a practical hierarchical clustering example. We will delve into the hierarchical clustering algorithm, compare its implementation in R, and discuss its significance in data mining. What is Hierarchical Clustering?Types of Hierarchical ClusteringAgglomerative Clustering HierarchicalDivisive Hierarchical ClusteringSupervised vs Unsupervised LearningWhy Hierarchical Clustering?No Need to Pre-specify Number of ClustersCaptures Nested ClustersFlexibility with Cluster ShapesDistance Metrics and Linkage CriteriaHandling OutliersRobustness to InitializationVisual InterpretationPractical ExampleSteps […]

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TL;DR Vanishing or exploding gradients are common training instabilities observed in foundation models. Real-time gradient-norm monitoring using experiment trackers like neptune.ai enables early detection and mitigation. Implementing stabilization techniques such as gradient clipping and optimizing weight initialization and learning rate schedules improves the training convergence and stability. As foundation models scale to billions or even trillions of parameters, they often exhibit training instabilities, particularly vanishing and exploding gradients. During the initial training phase (pre-training), it is common to […]

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As some of you know, I keep a running list of research papers I (want to) read and reference. About six months ago, I shared my 2024 list, which many readers found useful. So, I was thinking about doing this again. However, this time, I am incorporating that one piece of feedback kept coming up: “Can you organize the papers by topic instead of date?” The categories I came up with are: Reasoning Models – 1a. Training Reasoning […]

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