{"id":12289,"date":"2023-12-04T08:25:41","date_gmt":"2023-12-04T08:25:41","guid":{"rendered":"https:\/\/tokendices.com\/what-is-pytorch-and-how-does-it-work\/"},"modified":"2023-12-04T08:25:41","modified_gmt":"2023-12-04T08:25:41","slug":"what-is-pytorch-and-how-does-it-work","status":"publish","type":"post","link":"https:\/\/tokendices.com\/what-is-pytorch-and-how-does-it-work\/","title":{"rendered":"What Is PyTorch and How Does It Work?"},"content":{"rendered":"

Coinspeaker<\/a>
\nWhat Is PyTorch and How Does It Work?<\/a><\/p>\n

Launched in 2016 by Facebook AI Research (now AI Research at Meta Platforms Inc<\/a> (NYSE: META)), PyTorch has become one of the most popular machine-learning libraries among researchers and professionals.<\/p>\n

In this guide, we will explore what PyTorch is, how it works, discuss its key features, the problems it addresses, and the benefits it provides compared to other deep learning libraries.<\/p>\n

Additionally, we will delve into some of the most popular use cases of PyTorch in various areas.<\/p>\n

PyTorch Basics<\/h2>\n

PyTorch is an open-source machine learning library created to build deep learning neural networks by combining the Torch computational library oriented to GPUs with a high-level programming interface in Python. Its flexibility and ease of use have made it the leading framework in deep learning for the academic and research communities, supporting a wide range of neural network architectures.<\/p>\n

Developed in 2016 by researchers from Facebook AI Research (FAIR), PyTorch transitioned to the administration of the Linux Foundation in 2022 through the PyTorch Foundation, serving as a neutral forum to coordinate the future development of its ecosystem among the growing partner community.<\/p>\n

The library combines Torch’s efficient backend computational libraries, geared towards GPU-intensive neural training, with a high-level Python interface focused on facilitating rapid prototyping and experimentation. This significantly streamlines the debugging and prototyping of models.<\/p>\n

The two main components of PyTorch are tensors (multidimensional arrays for storing and manipulating data) and modules (fundamental blocks for building layers and network architectures). Both tensors and modules can run on CPUs or GPUs to accelerate calculations.<\/p>\n

PyTorch addresses various common problems in deep learning, such as:<\/p>\n