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Spotlight Feature Engineering for Reproducibility and Scalability
A strong feature engineering pipeline should maintain clean separation between data ingestion, cleaning, and transformation steps.
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Overly Commented AI Generated Code
Recently, I’ve been battling a new adversary: the overly-commented AI-generated code
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OpenAI News: o3-mini vs o1-mini comparison
Comparing the o3-mini and the o1-mini in terms of response latency, completion tokens, reasoning tokens
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Mastering Neovim Plugin Troubleshooting and Contribution
Neovim (nvim) is a powerful and extensible text editor, beloved by developers and machine learning engineers alike. However, like any complex tool, it can sometimes present challenges, especially when dealing with plugins. This guide aims to empower you to actively solve issues with any nvim plugin, contribute back to the community, and learn essential skills along the way.
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Mastering PCA and k-means Clustering: A Comprehensive Guide for Data Scientists
PCA simplifies datasets by reducing dimensionality, preserving variance. Data standardization ensures equal feature contribution, crucial for algorithms like PCA and clustering. Optimal cluster number can be determined using metrics like Elbow Method, Silhouette Score, and Calinski-Harabasz Index for meaningful data segmentation.