deep learning for computer vision deep learning is not just keyword abuzz in the industry and academicsit has thrown wide open whole new field of possibilities deep learning models are being employed in all sorts of use cases and domainssome of which we saw in the previous deep neural networks have tremendous potential to learn complex non-linear functionspatternsand representations their power is driving research in multiple fieldsincluding computer visionaudio-visual analysischatbots and natural language understandingto name few in this we touch on some of the advanced areas in the field of computer visionwhich have recently come into prominence with the advent of deep learning this includes real-world applications like image categorization and classification and the very popular concept of image artistic style transfer computer vision is all about the art and science of making machines understand high-level useful patterns and representations from images and videos so that it would be able to make intelligent decisions similar to what human would do upon observing its surroundings building on core concepts like convolutional neural networks and transfer learningthis provides you with glimpse into the forefront of deep learning research with several real-world case studies from computer vision this discusses convolutional neural networks through the task of image classification using publicly available datasets like cifarimagenetand mnist we will utilize our understanding of cnns to then take on the task of style transfer and understand how neural networks can be used to understand high-level features through this we cover the following topics in detailbrief overview of convolutional neural networks image classification using cnns from scratch transfer learningimage classification using pretrained models neural style transfer using cnns the code samplesjupyter notebooksand sample datasets for this are available in the github repository for this book at under the directory/folder for convolutional neural networks convolutional neural networks (cnnsare similar to the general neural networks we have discussed over the course of this book the additional explicit assumption of input being an image (tensoris what makes cnns optimized and different than the usual neural networks this explicit assumption is what allows us to design deep cnns while keeping the number of trainable parameters in check (in comparison to general neural networks(cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python