Deep Learning

This book provides a relatively comprehensive introduction to the basic concepts, algorithm principles and implementation framework of deep learning. The book has 9 chapters in total, which introduce the development history of deep learning, neural networks and deep neural networks, convolutional neural networks, recurrent neural networks, the application of deep learning in target detection and image description, generative adversarial networks, deep transfer learning and deep reinforcement learning, etc. It also provides application examples, aiming to help readers consolidate what they have learned through exercises and operational practices. This book can be used as a deep learning textbook for artificial intelligence, computer, automation, electronics and other related majors in colleges and universities. It can also be used for self-study by researchers in artificial intelligence, image processing, computer and other majors and for the majority of enthusiasts of artificial intelligence and deep learning technology. It can also be used as a teaching material for artificial intelligence technology training.