Applying CNN using PyTorch on MNIST handwritten Dataset

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

MNIST is the best to know for benchmark datasets in several deep learning applications. Taking a step forward many institutions and researchers have collaborated together to create MNIST like datasets with other kinds of data such as fashion, medical images, sign languages, skin cancers, colorectal cancer histology and skin cancer MNIST.

So, let’s build our image classification model using CNN in PyTorch. We will start with implementation in PyTorch. We will be implementing these models in google colab which provides free GPU to run these deep learning models.