Deep Learning with PyTorch : Siamese Network
In this 2-hour long guided-project course, you will learn how to implement a Siamese Network, you will train the network with the Triplet loss function. You will create Anchor, Positive and Negative image dataset, which will be the inputs of triplet loss function, through which the network will learn feature embeddings. Siamese Network have plethora of applications such as face recognition, signature checking, person re-identification, etc. In this project, you will train a simple Siamese Network for person re-identification.
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- المدة 3 ساعات hours
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In this 2-hour long guided-project course, you will learn how to implement a Siamese Network, you will train the network with the Triplet loss function. You will create Anchor, Positive and Negative image dataset, which will be the inputs of triplet loss function, through which the network will learn feature embeddings. Siamese Network have plethora of applications such as face recognition, signature checking, person re-identification, etc. In this project, you will train a simple Siamese Network for person re-identification.الوحدات
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Discover the essentials of Siamese Networks with our hands-on guided project, "Deep Learning with PyTorch: Siamese Network." This intermediate level course, designed for those in the IT and Computer Science fields, spans over 2 hours and provides a deep dive into the implementation and training of a Siamese Network using the Triplet loss function. You will create datasets with Anchor, Positive, and Negative images, crucial for training the network to learn feature embeddings. The skills acquired are applicable in various domains, including face recognition, signature verification, and person re-identification. Led by Coursera, this no-cost course is perfect for those looking to advance their deep learning expertise and apply it to real-world identification problems.
Parth Dhameliya