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Explainable Artificial Intelligence (XAI) for AI & ML Engineers

Analytics Vidhya

Introduction Hello AI&ML Engineers, as you all know, Artificial Intelligence (AI) and Machine Learning Engineering are the fastest growing filed, and almost all industries are adopting them to enhance and expedite their business decisions and needs; for the same, they are working on various aspects […].

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Exploring the benefits of artificial intelligence while maintaining digital sovereignty

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Around the world, organizations are evaluating and embracing artificial intelligence (AI) and machine learning (ML) to drive innovation and efficiency.

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Machine Learning (ML) vs Artificial Intelligence (AI)

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Machine learning (ML) and Artificial Intelligence (AI) have been receiving a lot of public interest in recent years, with both terms being …

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Breaking Down Social Bias in Artificial Intelligence Algorithms for Cardiovascular Risk Assessment

Analytics Vidhya

Hence, researchers are now exploring the potential of artificial intelligence (AI) and machine learning (ML) algorithms to improve […] The post Breaking Down Social Bias in Artificial Intelligence Algorithms for Cardiovascular Risk Assessment appeared first on Analytics Vidhya.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

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Top 14 Artificial Intelligence Startups to watch out for in 2021!

Analytics Vidhya

Overview Reducing company costs, generating customer insights & intelligence, and improving customer experiences are the three most popular ML and AI use cases Here. The post Top 14 Artificial Intelligence Startups to watch out for in 2021! appeared first on Analytics Vidhya.

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RAPIDS: Use GPU to Accelerate ML Models Easily

Analytics Vidhya

Introduction As Artificial Intelligence (AI) grows continuously, the demand for faster and more efficient computing power is increasing. Machine learning (ML) models can be computationally intensive, and training the models can take longer.

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