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AI Today Podcast: AI Glossary Series – Feature Reduction, Principal Component Analysis (PCA), and t-SNE
Manage episode 359885422 series 1539200
For a number of reasons, it can be important to reduce the number of variables or identified features in input training data so as to make training machine learning models faster and more accurate. But what are the techniques for doing this? In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Feature Reduction, Principal Component Analysis (PCA), and t-SNE, explain how they relate to AI and why it’s important to know about them.
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AI Today Podcast: AI Glossary Series – Feature Reduction, Principal Component Analysis (PCA), and t-SNE
AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion
Manage episode 359885422 series 1539200
For a number of reasons, it can be important to reduce the number of variables or identified features in input training data so as to make training machine learning models faster and more accurate. But what are the techniques for doing this? In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Feature Reduction, Principal Component Analysis (PCA), and t-SNE, explain how they relate to AI and why it’s important to know about them.
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