convolutional neural networks implementing basic cnn using pytorch the modern deep learning frameworks take care of the heavy lifting for bulk of the operations and constructs we need to develop cnn let' use simple example to illustrate how pytorch can be used to definetrainand evaluate cnn we will start with an mnist example that hosts collection of handwritten digit images our task is to classify given image as the digit between and #note computer vision tasks are very compute-intensive and usually require high-end hardware for training and evaluating large robust networks the mnist example we explore is miniature dataset that should be fairly easy for readers to reproduce in commodity hardware for more intensive examples in the we would recommend freewebbasedgpu-enabled compute instancelike kaggleor google colab both versions provide standard compute instance with ~ gb ram and gb gpu memorywith monthly quotas for experimentation purposesthese are great resources for more intensive experimentsreaders would need to explore deep learning instances on the cloud (aws/gcp/azureor custom hardware to startdownload the dataset available from com/ /digit-recognizer/data we will only use the training dataset that has the labels provided the training dataset will be further divided into training and validation now that we have the data readylet' begin the implementation by importing the required packages (listing -