Deep neural networks, also known as artificial neural networks (ANN), have become one of the most popular and successful approaches to machine learning tasks today. They can represent complex relationships in data, and they have been used to achieve state-of-the-art results in a wide variety of applications, including image classification, natural language processing, speech recognition, and robotics. In this blog post, we will show you how to build a deep neural network in PyTorch, a popular and powerful framework for machine learning. We will start with a brief overview of deep neural networks and their applications. Then, we will dive into the process of building a deep neural network in PyTorch, covering the basic components, steps, and code implementation. Finally, we will discuss how to test and evaluate the performance of the deep neural network on a validation or test dataset. By the end of this blog post, you will have a solid understanding of how to build a deep neural networ