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Samplers in PyTorch.

In this video, we are going to learn the concept of samplers in PyTorch. We will start with the definition of samplers, followed by the important samplers like the SubsetRandomSampler, RandomSampler, WeightedRandomSampler and also custom samplers. We will also see how some samplers for example the sequential sampler are just like shuffle = False in the PyTorch Dataloader. We also discuss how the WeightedRandomSampler could be used for unbalanced datasets. We then go on to implement all the samplers in Google Colab. We take a custom dataset which will help us visualize the working of the samplers. Connect to me on Instagram for questions:   / datumlearning   Connect on Facebook:   / datum.learning.1   For a 1-1 video chat on your projects or some topic in computer science, take my Fiverr gig: https://www.fiverr.com/share/2eoDk4