Marktechpost

AUGUST 7, 2024

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations: AI is increasingly critical in healthcare, especially in vitro diagnostics (IVD). The European IVDR recognizes software, including AI and ML algorithms, as part of IVDs.

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Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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Towards AI

OCTOBER 3, 2024

Neuroplasticity in AI Promising Research: a. Liquid Neural Networks: Research focuses on developing networks that can adapt continuously to changing data environments without catastrophic forgetting. By adjusting their parameters in real-time, liquid neural networks handle dynamic and time-varying data efficiently.

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The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. They work on complex problems that require advanced neural networks to analyse vast amounts of data.

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Defined.ai blog

AUGUST 10, 2023

For example, if your AI model were designed to predict future sales based on past data, the output would likely be a predictive score. This score represents the predicted sales, and its accuracy would depend on the data quality and the AI model’s efficiency. Maintaining data quality.

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Pickl AI

NOVEMBER 18, 2024

The article also addresses challenges like data quality and model complexity, highlighting the importance of ethical considerations in Machine Learning applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance.

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