PyTorch Data Preprocessing Step-by-Step: Load, Transform & Visualize
🚀 Welcome to My PyTorch Tutorial!
In this video, we dive deep into one of the most essential aspects of deep learning: loading datasets and applying transformations in PyTorch. Whether you're just starting out or looking to refine your skills, this tutorial will walk you through everything you need to know about working with datasets, preprocessing, and augmenting your data for better model performance.
What You'll Learn:
✅ How to load the CIFAR-10 dataset using torchvision.
✅ The importance of data transforms and preprocessing.
✅ How to use transforms.Compose to resize, normalize, and convert images to tensors.
✅ How to create and use a DataLoader for batching and shuffling data.
✅ How to visualize transformed images for better understanding.
✅ Bonus: Advanced transforms like random flips, rotations, and color jittering.
Code Highlights:
📂 Loading datasets with PyTorch
🛠 Applying transforms for preprocessing and data augmentation
📊 Visualizing transformed images in batches
💡 Why This Matters:
Preprocessing and augmentation play a key role in improving the robustness of your deep learning models. This video will help you build a strong foundation for handling data in PyTorch effectively.
🔗 Resources Mentioned in the Video:
CIFAR-10 Dataset: https://www.cs.toronto.edu/~kriz/cifa...
PyTorch Documentation: https://pytorch.org/docs/
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