Summary: Fundamentals of PyTorch

PyTorch is a popular and widely used Deep Learning (DL) framework that has caught the attention of serious AI researchers and practitioners around the world. PyTorch is a constantly developing DL framework with many exciting additions and features. In this article, we will go over some of the basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.

Tensors are at the heart of any DL framework and PyTorch provides tremendous flexibility to a programmer about how to create, combine, and process tensors as they flow through a network (called computational graph) paired with a relatively high-level, object-oriented API.

The nn.Module class is used to build any other neural classifier class in PyTorch and the Optimizer and Loss function are used to optimize the weights to achieve the lowest loss and best classification accuracy, respectively.

The five-step process for building and training a neural

Related articles

The Most Important Fundamentals of PyTorch you Should Know

Read the complete article at: www.datasciencecentral.com