image classification with cnns convolutional neural networks are prime examples of the potential and power of neural networks to learn detailed feature representations and patterns from images and perform complex tasksranging from object recognition to image classification and many more cnns have gone through tremendous research and advancements have led to more complex and power architectureslike vgg- vgg- inception and many more interesting models we begin with getting some hands-on experience with cnns by working on an image classification problem we shared an example of cnn based classification in through the notebookbonus classifying handwritten digits using deep cnns ipynbwhich talks about classifying and predicting human handwritten digits by leveraging cnn based deep learning in case you haven' gone through itdo not worry as we will go through detailed example here for our deep learning needswe will be utilizing the keras framework with the tensorflow backendsimilar to what we used in the previous problem statement given set of images containing real-world objectsit is fairly easy for humans to recognize them our task here is to build multiclass ( classes or categoriesimage classifier that can identify the correct class label of given image for this taskwe will be utilizing the cifar dataset dataset the cifar dataset is collection of tiny labeled images spanning across different classes the dataset was collected by alex krizhevskyvinod nairand geoffrey hinton and is available at this dataset contains tiny images of size with , training and , test samples each image can fall into one and only one of the following classes automobile airplane bird cat deer dog frog horse ship truck each class is mutually exclusive there is another larger version of the dataset called the cifar for the purpose of this sectionwe will consider the cifar dataset we would be accessing the cifar dataset through the keras datasets module download the required files if they are not already present