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PyTorch is a deep learning library designed to simplify model training and inference with a high-level API.
PyTorch is an open-source deep learning library originally developed by Meta and now supported through the Linux Foundation. It provides a high-level API for building and training deep neural networks while relying on optimized low-level implementations for core operations and architectures (e.g., Transformers and SGD). This design helps simplify model training and inference to relatively few lines of code. Its purpose is to make deep learning development efficient and flexible by using tensors as the fundamental data type, supporting GPU acceleration via CUDA (and other backends such as ROCm and Metal), and enabling automatic differentiation through Autograd. During the forward pass, Autograd records operations to build a computation graph, and then backpropagation computes gradients needed for learning. PyTorch also includes tools for defining neural networks (via the nn module) and saving/loading models using its own serialized file format.
PyTorch is a deep learning library designed to simplify model training and inference with a high-level API.
It uses tensors as the core data structure and supports GPU acceleration (e.g., CUDA) for faster computation.
Autograd enables automatic differentiation by building a computation graph during the forward pass and performing backpropagation for gradient-based learning.
An open-source deep learning library that provides tools for building, training, and running neural networks efficiently.
PyTorch’s fundamental data type for storing and operating on homogeneous multidimensional arrays of numbers.
PyTorch’s automatic differentiation system that records operations in a computation graph and enables backpropagation.
The PyTorch module used to define neural networks and provide common layers and building blocks.
A GPU computing platform that PyTorch can leverage to speed up training and inference on NVIDIA hardware.
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