PyTorch is an open-source machine learning library used for building and training neural networks. It was developed primarily by Facebook's artificial intelligence research group, and is one of the most widely used machine learning frameworks in the world.
PyTorch allows developers to build and train neural networks using dynamic computation graphs. This means that the structure of the neural network can change during runtime, making it easier to build more complex models.
PyTorch also provides a wide range of pre-built neural network layers and functions, as well as tools for data loading and transformation, optimization, and visualization. It also includes tools for distributed training, allowing for the training of large neural networks across multiple devices.
PyTorch is written in Python, but also includes support for other programming languages such as C++ and Java. Additionally, it has a large and active community of developers, making it easy to find tutorials, sample code, and other resources to help you get started with using PyTorch.
PyTorch is an open-source machine learning framework that is primarily used for building and training neural networks. It was developed by Facebook's AI research team and is widely used in the research community and in industry.
PyTorch provides a wide range of tools and functionality for developing and training machine learning models. It offers a dynamic computational graph, which allows for flexible model building and debugging, as well as efficient memory usage. It also provides automatic differentiation, which makes it easy to compute gradients for optimizing model parameters.
PyTorch has a large and active community of users, which has contributed to the development of many third-party libraries and extensions. This includes PyTorch Lightning, which provides a high-level interface for building complex machine learning models, and PyTorch Geometric, which provides tools for working with graphs and other structured data.
Overall, PyTorch is a powerful and flexible machine learning framework that can be used for a wide range of tasks, including computer vision, natural language processing, and reinforcement learning.