GS.PyTorch

settings.json {     "workbench.colorTheme" : "Default High Contrast" ,     "cmake.pinnedCommands" : [         "workbench.action.tasks.configureTaskRunner" ,         "workbench.action.tasks.runTask"     ],     "terminal.integrated.profiles.windows" : {                 "PowerShell" : {             "source" : "PowerShell" ,             "icon" : "terminal-powershell"         },         "Command Prompt" : {             "path" : [                 "${env:windir} \\ Sysnative \\ cmd.exe" ,                 "${env:windir} \\ System32 \\ cmd.exe"             ],             "args" : [],         ...

https://pytorch.org/ https://pytorch.org/get-started/locally/ https://pytorch.org/docs/stable/index.html PyTorch is an open-source machine learning library developed by Facebook's AI Research lab (FAIR) that has gained significant popularity in the fields of artificial intelligence and deep learning. It is primarily used for applications such as computer vision and natural language processing. Here’s an in-depth overview of its features, functionalities, and use cases: Key Features of PyTorch Dynamic Computation Graphs: One of the standout features of PyTorch is its dynamic computation graph, also known as "define-by-run". This means that the graph is built on-the-fly as operations are executed, allowing for more flexibility and ease of debugging. This is particularly beneficial when working with models that require varying input sizes or when implementing complex architectures. Tensors: At the core of PyTorch is the Tensor library, which provides multi-dimensional arrays...