Recent advances in deep learning have led to groundbreaking advances in many fields, including computer vision and naturallanguage processing. This course aims to equip students withpractical skills and theoretical knowledge to leverage cutting-edgedeep neural network architectures and algorithms to solve real-worldchallenges. Students will gain a thorough understanding of deeplearning fundamentals such as network architecture design, activation functions, loss functions, optimization algorithms, andregularization techniques that collectively enable neural networks tolearn complex patterns and representations from data. Students willthen gain practical knowledge on deploying deep learning models,conducting exper experiments, and optimizing model performance through throughhands-on experience with real-world datasets using the Pythonprogramming language and the PyTorch framework.