Artificial Intelligence have revolutionized many fields in science. Deep learning methods have been proved powerful in approximating highly complex patterns from masses amount of data. In recent years, increasing interest has been drawn to the application of deep learning methods in chemical and biological engineering problems, including molecular and reaction property prediction, drug discovery, protein structure prediction, model predictive control, etc. Deep learning is viewed as an essential skill for the next generation of chemical engineers and as a promising solution to many challenges in chemistry, biology, energy and health. This course introduces the basic concepts and methods in deep learning, including deep neural networks, backpropagation, convolutional and recurrent neural networks, generative models, with a focus on their applications in the problems mentioned above. At the end of this course, students will be able to apply these methods to solve real-world problems in industry or academic research.