This book first outlines the basic concepts and development history related to artificial intelligence and deep learning; then it introduces in detail the basic theories and algorithms of deep learning, including key technologies of neural networks, main framework and application examples of convolutional neural networks, models and applications of recurrent neural networks and unsupervised learning of deep neural networks, parameter optimization methods of deep neural networks, lightweight schemes of deep learning models, and deep learning cases at the mobile end; after that, the book describes the basic theories and algorithms of reinforcement learning, including traditional reinforcement learning methods and their derivative algorithms as well as new multi-agent or multi-task learning models; finally, it introduces specific algorithms and applications of deep reinforcement learning, the concept of transfer learning and its application in deep learning and reinforcement learning.