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Self Supervised Learning Models to Predict Early COVID-19 Deterioration by Facebook AI

Analytics Vidhya

ArticleVideos Overview Facebook AI and NYU Health Predictive Unit have developed machine learning models that can help doctors predict how a patient’s condition may. The post Self Supervised Learning Models to Predict Early COVID-19 Deterioration by Facebook AI appeared first on Analytics Vidhya.

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The Marvels of Generative AI: Key Concepts and Use Cases

Data Science Dojo

This isn’t the plot of a sci-fi novel but the reality of generative artificial intelligence (AI). Generative AI is transforming how we approach creativity and problem-solving across various sectors. What is Generative AI? For example, in biotechnology, generative AI can design novel protein sequences for therapies.

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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

In the recent discussion and advancements surrounding artificial intelligence, there’s a notable dialogue between discriminative and generative AI approaches. These methodologies represent distinct paradigms in AI, each with unique capabilities and applications. What is Generative AI?

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PositiveGrid introduces SparkAI for real-time tone modeling

Dataconomy

PositiveGrid, a manufacturer of digital music technology, has integrated artificial intelligence into its Spark series amplifiers with SparkAI, an AI-powered tone generator. Using deep learning and transformer-based models, SparkAI processes extensive audio datasets to analyze tonal characteristics and generate realistic guitar sounds.

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Understanding Autoencoders in Deep Learning

Pickl AI

Summary: Autoencoders are powerful neural networks used for deep learning. Their applications include dimensionality reduction, feature learning, noise reduction, and generative modelling. By the end, you’ll understand why autoencoders are essential tools in Deep Learning and how they can be applied across different fields.

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How to tackle lack of data: an overview on transfer learning

Data Science Blog

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

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How Should Self-Supervised Learning Models Represent Their Data?

NYU Center for Data Science

Self-supervised learning (SSL) has emerged as a powerful technique for training deep neural networks without extensive labeled data. Rudner, among others, and “ To Compress or Not to Compress — Self-Supervised Learning and Information Theory: A Review.”