Using Dataset Classes in PyTorch
In machine learning and deep learning problems, a lot of effort goes into preparing the data. Data is usually messy and needs to be preprocessed before it can be used for training a model. If the data is not prepared correctly, the model won’t be able to generalize well.
Some of the common steps required for data preprocessing include:
- Data normalization: This includes normalizing the data between a range of values in a dataset.
- Data augmentation: This includes generating new samples from existing ones by adding noise or shifts in features to make them more diverse.
Data preparation is a crucial step in any machine learning pipeline. PyTorch brings along a lot of modules such as torchvision which provides datasets