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20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% billion by the end of 2025. billion by the end of 2025.

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20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% billion by the end of 2025. billion by the end of 2025.

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Can AI help you binge books? BingeBooks is a new service to do … exactly that

TechCrunch

The pandemic has been terrible for many industries, but the book industry has gotten a rare reprieve in an otherwise dismal past decade. That surge in interest in books has also led to a surge in interest from founders to rethink aspects of the reading experience. No boring book clubs. The first rule of BookClub?

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Hopper raises $170M and partners with Capital One on a new cardholder travel booking portal

TechCrunch

.” That pre-COVID transformation that Pichette is referring to is Hopper’s shift from being essentially a machine learning-powered lowest fare finder to what co-founder and CEO Fred Lalonde says is really much more of a fintech company. “Price freeze is a good example. . “Price freeze is a good example.

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Freshly (un)retired, Gary McGraw takes on machine-learning security (Q&A)

The Parallax

The mirror, built by the CareOS subsidiary of the French tech company Baracoda , offers personalized recommendations guided by Google’s TensorFlow Lite machine-learning algorithm platform. READ MORE ON MACHINE LEARNING. How Facebook fights fake news with machine learning and human insights.

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Machine Learning and the Production Gap

O'Reilly Media - Ideas

The biggest problem facing machine learning today isn’t the need for better algorithms; it isn’t the need for more computing power to train models; it isn’t even the need for more skilled practitioners. It’s getting machine learning from the researcher’s laptop to production.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO

Before LLMs and diffusion models, organizations had to invest a significant amount of time, effort, and resources into developing custom machine-learning models to solve difficult problems. In many cases, this eliminates the need for specialized teams, extensive data labeling, and complex machine-learning pipelines.