A beginner-friendly implementation guide.

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Daily Dose of Data Science | Avi Chawla | Substack

This guide provides a beginner-friendly implementation of a Siamese network using the MNIST dataset. It covers dataset creation, defining the network, applying contrastive loss, and training the model. The post includes detailed steps for creating image pairs, defining a custom dataset class, and testing the model's performance. Key takeaway: Siamese networks require labeled data, but methods to handle unlabeled data are in discussion.

Implementing a Siamese Network

For those wanting to develop “Industry ML” expertise: