The purpose of this course is to familiarize students with state-of-the-art deep learning techniques adopted by the industry, such as MPL, CNN, LSTM, Transformer, BERT, GPT-3, VAE, GAN, CLIP, DALL-E 2, and more. Students will learn theoretical and practical concepts of deep neural networks, including optimization, inference, architecture, and applications. After completing this course, students should be able to develop and train deep neural networks, reproduce research results, and conduct original research. Additionally, students will use the Python programming language to implement deep learning applications through the TensorFlow Keras or PyTorch packages.