Deep learning is a type of machine learning technique that uses algorithms based on artificial neural networks. In the last decade, deep learning has shown promising results in many application areas, ranging from communication systems, signal processing, computer vision, and natural language processing. One of the main drivers for the advancement of this field and its numerous applications is the rise of a simple application programming interface (API) for the implementation of deep learning algorithms. This led to the democratization of technology and its usage. Pytorch is one of the most popular programming libraries in Python for the experiment and development of deep learning algorithms. It is developed by Facebook and provides implementations of many state-of-the-art deep learning models. In this short course, the basic concepts that include tensors, automatic differentiation, and technique for creating simple fully connected neural network layers will be covered. Students will then apply this concept to create a simple neural network model known as multilayer perceptron (MLP) to solve regression and classification problems. After this short course, students are expected to be able to use Pytorch to solve modeling problems related to classification and regression with tabular data.