convolutional neural networks #collect dog images for img_path in glob glob(os path join(new_path,"train","dog""jpg"))[: ]images append(mpimg imread(img_path)#plot grid of cats and dogs plt figure(figsize=( , )columns for iimage in enumerate(images)plt subplot(len(imagescolumns columnsi plt imshow(imageplots of random images from the training set for computer vision experimentswe would always apply numerous transformations on the raw dataset core reason for this is that most images used in an experiment would be of different sizes alsoat timeswe might need to add more training samples by augmenting the existing samples some examples would include augmenting more training samples with random rotationscropping images from centerflipping across axisstandardizing pixel valuesetc pytorch provides handy functionality to compose several such transformations and orchestrate them on training and validation samples in listing - we compose