Deep learning techniques have revolutionized computer vision, making it possible to reliably understand what an image represents semantically, not just treat it as an array of pixels. Leo Dirac demonstrates how to apply the latest deep learning techniques to semantically understand images. You’ll learn what embeddings are, how to extract them from your images using deep convolutional neural networks (CNNs), and how they can be used to cluster and classify large datasets of images, as Leo walks you through a complete example, from preparing the images to visualizing the discovered clusters in a notebook. Along the way, you’ll see how deep learning networks produce vector embeddings as a side effect of their main classification task and how these can be used for related tasks such as measuring similarity.
Leo Dirac is a principal engineer on the Amazon AI team at Amazon Web Services. Previously, he led the engineering team that launched the Amazon Machine Learning service. Leo has a background in physics. He started writing software professionally in the 1980s. In 2012, he became fascinated with deep learning and has been building systems with it ever since.
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