This course, Discover Deep Learning with PyTorch, offers a comprehensive introduction to the field of deep learning and its applications using the PyTorch framework. Over the past few years, neural networks have emerged as a prominent area of research in Artificial Intelligence, successfully addressing complex problems such as image classification, language translation, and even defeating human champions in games like Alpha Go. PyTorch stands out as a leading deep learning framework due to its combination of power and user-friendliness. Throughout this course, you will gain a solid understanding of the fundamental concepts of neural networks, starting with an exploration of the PyTorch library and its practical applications for deep learning. Building upon this foundation, you will then proceed to construct your first neural network, specifically designed to predict digits from an MNIST dataset. As you progress, you will delve deeper into the world of deep learning models. The course will guide you through the intricacies of convolutional neural networks, enabling you to construct more sophisticated models that yield highly accurate results. You will also learn how to evaluate these results and employ various techniques to enhance their performance, including regularization and transfer learning. By the end of this course, you will have gained the confidence and knowledge necessary to further explore the realm of neural networks. Whether you aspire to pursue advanced studies or apply deep learning techniques in practical settings, this course will equip you with the skills needed to progress in your journey.