pyTorch
PyTorch is an open-source machine learning framework that was developed by Facebook's AI Research (FAIR) team. It is widely used for building and training deep learning models, including neural networks, for a wide range of applications, such as computer vision, natural language processing, and reinforcement learning.
PyTorch is built on top of the Torch framework, which is a scientific computing framework that provides a range of numerical algorithms and data structures. PyTorch extends Torch with features and capabilities that make it a powerful tool for deep learning, including:
Dynamic computational graphs: PyTorch uses dynamic computational graphs, which allows developers to modify the model structure and computation during runtime. This makes it easier to write and debug complex models.
GPU acceleration: PyTorch provides support for GPU acceleration, which makes it possible to train large deep learning models quickly.
Distributed training: PyTorch supports distributed training across multiple devices and nodes, making it possible to train large models on large datasets.
High-level APIs: PyTorch provides high-level APIs, such as torch.nn and torchvision, that make it easy to build and train deep learning models, without requiring deep knowledge of the underlying algorithms and architectures.
Flexibility: PyTorch provides a flexible programming model that allows developers to work with both high-level and low-level APIs, giving them greater control over the training process.
PyTorch is a powerful machine learning framework that provides a range of features and capabilities for building and training deep learning models. Its popularity and community support have made it a popular choice for data scientists and developers working in machine learning and artificial intelligence.
PyTorch is built on top of the Torch framework, which is a scientific computing framework that provides a range of numerical algorithms and data structures. PyTorch extends Torch with features and capabilities that make it a powerful tool for deep learning, including:
Dynamic computational graphs: PyTorch uses dynamic computational graphs, which allows developers to modify the model structure and computation during runtime. This makes it easier to write and debug complex models.
GPU acceleration: PyTorch provides support for GPU acceleration, which makes it possible to train large deep learning models quickly.
Distributed training: PyTorch supports distributed training across multiple devices and nodes, making it possible to train large models on large datasets.
High-level APIs: PyTorch provides high-level APIs, such as torch.nn and torchvision, that make it easy to build and train deep learning models, without requiring deep knowledge of the underlying algorithms and architectures.
Flexibility: PyTorch provides a flexible programming model that allows developers to work with both high-level and low-level APIs, giving them greater control over the training process.
PyTorch is a powerful machine learning framework that provides a range of features and capabilities for building and training deep learning models. Its popularity and community support have made it a popular choice for data scientists and developers working in machine learning and artificial intelligence.
Listing Details
Main Functions
machine learning toolkits
Top Features
provides a range of numerical algorithms and data structures for building and training deep learning models
Typical customers
Small businesses, Mid size businesses, Large enterprises
Platform
Windows, MAC, Linux
Price
Free
Website
Tags
