"Deep Learning for Engineers introduces fundamental principles of deep learning along with the explanation of basic elements required for understanding and applying deep learning models. As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures and their practical implementations in science and engineering are also discussed"--