This course covers a wide range of topics from the basics to advanced topics of deep learning, and I liked the hands-on approach using PyTorch. It covered a variety of topics, including setting up the PyTorch environment, basic concepts of deep learning, loss functions, gradient descent, activation functions, optimization, regularization, learning rate schedulers, initialization, standardization, CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), and the latest topics such as Attention and Transformers. The course is designed to be easily accessible to beginners, and each section consists of various exercises along with theoretical explanations, so I especially liked the fact that learners can experience the principles of deep learning by writing code themselves. In particular, it is recommended not only for those who are new to the field of deep learning, but also for those who want to refresh their basic knowledge, as it allows them to learn step-by-step from basic concepts that can be applied immediately in practice to advanced topics. Each topic is covered in depth with ample practice and examples, which I believe will allow learners to comprehensively understand the various aspects of deep learning and develop the ability to apply them to solving real-world problems. The systematic organization of the lectures and the practice-oriented approach provide learners with the practical experience necessary to actually utilize deep learning technology, and I highly recommend this course to anyone interested in the field of deep learning.