Text Embeddings and Semantic Space
Kyle discusses the power of text embeddings and how they represent positions in a semantic space, allowing machine learning algorithms to map similar ideas together. He explains how handwritten digits in the MNS dataset can be clustered based on their similarities, and how these algorithms can learn subtle differences on their own. Kyle also touches on the importance of handcrafted features in fraud detection and how machine learning is making the process easier while raising expectations.In this clip
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