Foundations of PyTorch and Linear Regression

Building a solid foundation in deep learning requires a clear grasp of how data flows through tensors and how gradients update model parameters. This text-based course guides you through the essential mathematics and programming concepts needed to construct and train predictive models from scratch. You will start with the absolute basics of tensor manipulation before moving on to automatic differentiation and optimization. By reading through structured explanations and analyzing clean code snippets, you will understand how to translate mathematical regression formulas into functional PyTorch workflows. What you will learn: Understand the core architecture of tensors and how to manipulate multi-dimensional data; Master the autograd engine to calculate gradients automatically for model optimization; Configure loss functions and optimization algorithms to train models effectively; Build and train a linear regression model using modern PyTorch design patterns; Practice modern debugging techniques and apply proper type hinting to your deep learning code. This course begins with fundamental definitions of tensors and computational graphs before leading you step-by-step through the training loop of a linear regression model. It is designed specifically for beginners with basic Python knowledge who want to understand the inner workings of modern machine learning frameworks. Start your journey into deep learning today by mastering the foundational building blocks of PyTorch.