Machine Learning Articles

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Mohamed Lazzouni Mohamed Lazzouni
Updated on June 14, 2023

Voice Cloning: What It Is and Why It’s Scary

Voice cloning, a potent new use of artificial intelligence, copies a person's voice with eerie accuracy.

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Jason Beres Jason Beres
Updated on June 13, 2023

A Software Developer’s Guide to Generative AI

Accelerate your code development and automate repetitive tasks with generative AI tools like GitHub Copilot. Here’s how.

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Sara A. Metwalli Sara A. Metwalli
Updated on June 13, 2023

SQL vs. NoSQL: What’s the Difference?

SQL and NoSQL — which one is best for your data? These are their major differences.

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Cassie Kozyrkov Cassie Kozyrkov
Updated on June 07, 2023

What Does It Mean to ‘Work With AI’?

As artificial intelligence has become useful for solving everyday problems, it has left the exclusive realm of research and become an applied technology. So, who gets to claim it?

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Ellen Glover Ellen Glover
Updated on June 05, 2023

What Is Image Recognition?

Image recognition gives machines the power to “see” and understand visual data.

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Sean Benhur Sean Benhur
Updated on May 31, 2023

Guide to Meta Learning

Meta learning helps models learn new, unseen tasks on their own.

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Will Koehrsen Will Koehrsen
Updated on May 30, 2023

How to Mind Goodhart’s Law and Avoid Unintended Consequences

One of the first steps to solving a problem — data science or otherwise — is determining the right metrics to gauge success. Choose wisely.

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Manasi Vartak Manasi Vartak
Updated on May 18, 2023

Responsible AI Explained

Responsible AI is a framework for developing AI safely and ethically based on the five core principles of fairness, transparency, accountability, privacy and safety. Here’s what you need to know. 

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Badreesh Shetty Badreesh Shetty
Updated on May 16, 2023

An In-Depth Guide to How Recommender Systems Work

You might also like to know how programs know what you want before you do.

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Srishti Saha Srishti Saha
Updated on May 16, 2023

Covariance vs. Correlation: Differences to Know

Most articles on probability and statistics presume a basic understanding of covariance and correlation, but what do these concepts actually mean?