Tensor computation, GPU acceleration, and dynamic computational graph for efficient machine learning development.
Build and modify neural networks on the fly with PyTorch's dynamic computational graph, allowing for rapid prototyping and development.
Take advantage of GPU acceleration for faster tensor computations and improved performance in machine learning tasks.
Distribute training across multiple GPUs and machines with PyTorch's built-in support for distributed training, speeding up large-scale machine learning tasks.
Use pre-built modules for common tasks like computer vision and natural language processing, reducing development time and effort.
Benefit from PyTorch's large and active community, with extensive documentation, tutorials, and pre-built models available for various tasks.
Build and train neural networks for image classification tasks.
Develop natural language processing models for text classification and sentiment analysis.
Create custom machine learning models for specific business needs.
Install PyTorch using pip or conda.
Import PyTorch and start building your first neural network.
Use pre-built modules and tutorials to speed up development.