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Illuminating the Black Box of Textual GenAI | by Anthony Alcaraz | Dec, 2023

[ad_1] The need for insights LLMs like ChatGPT, Claude 2, Gemini, and Mistral captivate the world with their articulateness and erudition. Yet these large language models remain black boxes, concealing the intricate machinery powering their responses. Their prowess at generating human-quality text outstrips our prowess at understanding how their machine minds function. But as artificial…

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A Guide to 21 Feature Importance Methods and Packages in Machine Learning (with Code) | by Theophano Mitsa | Dec, 2023

[ad_1] From the OmniXAI, Shapash, and Dalex interpretability packages to the Boruta, Relief, and Random Forest feature selection algorithms Image created by the author at DALL-E“We are our choices.” —Jean-Paul Sartre We live in the era of artificial intelligence, mostly because of the incredible advancement of Large Language Models (LLMs). As important as it…

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Visualizing trade flow in Python maps — Part I: Bi-directional trade flow maps | by Himalaya Bir Shrestha | Dec, 2023

[ad_1] In the trade flow maps, I aimed to represent two-way trade relationships between countries. For example, the export from Nepal to India would be represented by the first arrow (A1-A2) and the import by Nepal from India would be represented by a second arrow (A3-A4). In this way, each country pair relationship would require…

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Comparing Outlier Detection Methods | by John Andrews | Dec, 2023

[ad_1] Using batting stats from Major League Baseball’s 2023 season Shohei Ohtani, photo by Erik Drost on Flikr, CC BY 2.0Outlier detection is an unsupervised machine learning task to identify anomalies (unusual observations) within a given data set. This task is helpful in many real-world cases where our available dataset is already “contaminated” by anomalies.…

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