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ML and AI Model Explainability and Interpretability

Analytics Vidhya

In this article, we dive into the concepts of machine learning and artificial intelligence model explainability and interpretability. Through tools like LIME and SHAP, we demonstrate how to gain insights […] The post ML and AI Model Explainability and Interpretability appeared first on Analytics Vidhya.

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What is Bagging in Machine Learning?

Analytics Vidhya

Introduction In the field of machine learning, developing robust and accurate predictive models is a primary objective. Ensemble learning techniques excel at enhancing model performance, with bagging, short for bootstrap aggregating, playing a crucial role in reducing variance and improving model stability.

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Explaining MLOps using MLflow Tool

Analytics Vidhya

These tool help to improve the deployment process for robust machine-learning projects. The post Explaining MLOps using MLflow Tool appeared first on Analytics Vidhya. Introduction In this article, we will be seeing MLOps from the dimension of one of the powerful tools that make it easy to implement.

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Ordnance Survey: Navigating the role of AI and ethical considerations in geospatial technology

AI News

As we approach a new year filled with potential, the landscape of technology, particularly artificial intelligence (AI) and machine learning (ML), is on the brink of significant transformation. The Ethical Frontier The rapid evolution of AI brings with it an urgent need for ethical considerations.

Big Data 286
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Explainable AI: Demystifying the Black Box Models

Analytics Vidhya

Introduction In today’s data-driven world, machine learning is playing an increasingly prominent role in various industries. Explainable AI aims to make machine learning models more transparent to clients, patients, or loan applicants, helping build trust and social acceptance of these systems.

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Nordic Startup IntuiCell Unveils World’s First Digital Nervous System for AI

Unite.AI

IntuiCell , a spin-out from Lund University, revealed on March 19, 2025, that they have successfully engineered AI that learns and adapts like biological organisms, potentially rendering current AI paradigms obsolete in many applications. The practical application of this technology reflects its biological inspiration.

Robotics 261
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XElemNet: A Machine Learning Framework that Applies a Suite of Explainable AI (XAI) for Deep Neural Networks in Materials Science

Marktechpost

From tasks like predicting material properties to optimizing compositions, deep learning has accelerated material design and facilitated exploration in expansive materials spaces. However, explainability is an issue as they are ‘black boxes,’ so to say, hiding their inner working. Check out the Paper.