On #TutorialTuesdays we revisit resources to power your PyTorch learning journey. This week’s beginner basics text based lesson is in two parts — Datasets & Dataloaders and Transforms. Read on for highlights of what you’ll learn: 🧵👇bit.ly/3D8cmyy
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In PyTorch Datasets & Dataloaders Tutorial: Code for processing data samples needs to be organized. Dataset code should be decoupled from model training code for better readability & modularity. Learn in 5 steps.
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In this PyTorch Datasets & Dataloaders Tutorial, learn about datasets:
1) Loading
2) Iterating & visualizing
3) Creating a custom dataset
4) Preparing your data for training with DataLoaders
5) Iterating through the DataLoader
Give it a try: bit.ly/3D8cmyy
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1) Loading
2) Iterating & visualizing
3) Creating a custom dataset
4) Preparing your data for training with DataLoaders
5) Iterating through the DataLoader
Give it a try: bit.ly/3D8cmyy
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In PyTorch Transforms Tutorial: Data doesn’t always come in its final processed form that’s required to train machine learning algorithms. Use transforms to perform some manipulation of the data and make it suitable for training. Learn how: bit.ly/3gz9YJz
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