Welcome to the Introduction to Deep Learning with PyTorch course. Deep learning has become ubiquitous in various applications such as smartphone cameras, voice assistants, and self-driving cars. It has even made significant contributions in fields like protein structure discovery and defeating human players in the game of Go. In this course, we will explore the capabilities of this powerful technology and learn how to effectively utilize it with PyTorch, a widely used deep learning library. The course begins by clarifying the distinction between deep learning and traditional machine learning approaches. We will introduce the concept of neural networks and delve into the training process. You will gain a comprehensive understanding of how to construct a training loop and develop loss functions for both regression and classification problems. Additionally, we will leverage the capabilities of PyTorch to calculate derivatives, a crucial aspect of neural network training. The second part of the course focuses on evaluating and enhancing your models. We will explore various hyperparameters that can be adjusted to optimize model performance. As you become familiar with the different components of a neural network, you will be able to create more intricate and sophisticated architectures. To assess the effectiveness of your models, we will utilize TorchMetrics, a PyTorch library specifically designed for model evaluation. By the conclusion of this course, you will possess the skills to employ PyTorch for solving classification and regression problems using deep learning techniques. You will be equipped to tackle both tabular and image data, enabling you to address a wide range of real-world challenges.