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PyTorch is a machine learning framework based on the Torch library. Its flexibility and user-friendliness have accumulated a massive user base in both industry and academia. Most modern research code is written in PyTorch. In this course, we will provide a step-by-step comprehensive coverage of modern applications in PyTorch. The course topics can be broadly categorized into three popular applications: computer vision, natural language processing, and reinforcement learning. We will study the experimental details of using PyTorch for a wide variety of tasks such as image/video classification, object detection, semantic segmentation, text classification, sequence-to-sequence translation, visual question answering, and DQN. In terms of modern deep learning architectures, we will cover 2D/3D convolutional neural networks, recurrent neural networks, long-short term memory, transformers, and encoder-decoder networks. Students will be technically prepared for more advanced courses in different application after taking this course.