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Deep metric learning is a technique that focuses on determining the similarity or dissimilarity between data through the use of a distance metric. One approach to deep metric learning is the use of Siamese networks. When training a Siamese network for deep metric learning, two common loss functions are used: Contrastive Loss and Triplet Loss.
In this DataHour session, we will learn about training a Siamese network using the Triplets loss function, with the sample image dataset. Which can be further used to build a visual search engine, object reidentifications, and face verification tasks.
Prerequisites: The zeal for learning new technologies, and good to have a conceptual understanding of how neural network works.
Udaya A S
Data Scientist at Jio
Udaya is a highly skilled data scientist at Jio, with a strong track record of delivering high-quality, data-driven solutions that meet business needs. With five years of experience in the field, Udaya specializes in developing recommendation engines and computer vision models for production-level applications. He has deep expertise in data pre-processing, feature engineering, modelling, and deployment, which has allowed him to successfully deliver complex projects across a range of industries. Udaya's ability to develop innovative solutions, combined with his attention to detail and analytical skills, make him a valuable asset to any team.
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