In this post, we’ll deal with one of the most challenging problems in the fields of Machine Learning and Deep Learning: the struggle of loading and handling different types of data.

Say you’re already familiar with coding Neural Networks in PyTorch, and now you’re working on predicting a number using the MNIST dataset with a multilayer perceptron. In that case, you probably used the torch DataLoader class to directly load and convert the images to tensors. But now, in this post, we’ll learn how to go beyond the DataLoader class and follow the best practices that can be used while dealing with various forms of data, such as CSV files, images, text, etc. Below are the topics that we’ll be covering.

• Working on Datasets
• Data Loading in PyTorch
• Looking at the MNIST Dataset in-Depth
• Transforms and Rescaling the Data
• Creating Custom Datasets in PyTorch
• Summary

#python #pytorch #machine-learning #deep-learning #developer