The class is designed to introduce students to the background knowledge of deep learning and its applications in many fields. Key concepts of building neural networks will be studied, and the implementations will be demonstrated. The first part of the course will cover the basics of neural networks, including perceptron, forward and backward propagation, regularization, and normalization. Moreover, deep learning models, such as convolutional neural networks, recurrent neural networks as well as sequence models involving attention mechanisms will be included in this course. Also, pre-trained models, i.e. word2vec and BERT, will be introduced. Finally, several domain experts will be invited to present their expertise and experience in many different fields, e.g. computer vision, music, and finance. The final project will involve training a neural network and apply it to a real-world problem. After taking the course, students will obtain the skills for building neural networks on practical problems.