PyTorch Datasets, DataLoaders and Transforms - GPU Series Part 3

Explore the essential components of data handling in PyTorch for deep learning tasks in this 30-minute video tutorial. Dive into the Fashion-MNIST dataset, master the usage of PyTorch's Dataset and DataLoader classes, and learn how to efficiently move tensors to CUDA for GPU-accelerated training. Discover techniques for transforming input data using PyTorch's transform functions and implement target transformations with lambda functions for one-hot encoded tensors. Gain practical insights on preparing and visualizing image datasets, creating minibatches, and optimizing your deep learning workflow for improved performance and efficiency.

A background on Fashion-MNIST
PyTorch's Dataset and DataLoaders usage example
Moving tensors to CUDA for GPU training
Transform input data with PyTorch's transform functions
Using target_transform for one hot encoded tensors lambda function