✳flâneur — a map of the web's best reading

Learning PyTorch with Examples — PyTorch Tutorials 2.3.0+cu121 documentation

pytorch.org · 5,055 words · saved by 1 readers

This is one of our older PyTorch tutorials. You can view our latest beginner content in Learn the Basics. This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. At its core, PyTorch provides two main features: An n-dimensional Tensor, similar to numpy but can run on GPUs Automatic differentiation for building and training neural networks We will use a problem of fitting 𝑦 = sin ⁡ ( 𝑥 ) y=sin(x) with a third order polynomial as our running example. The network will have four parameters, and will be trained with gradient descent to fit random data by minimizing the Euclidean distance between the network output and the true output. NOTE You can browse the individual examples at the end of this page. Table of Contents Tensors Warm-up: numpy PyTorch: Tensors Autograd PyTorch: Tensors and autograd PyTorch: Defining new autograd functions nn module PyTorch: nn PyTorch: optim PyTorch: Custom nn Modules PyTorch: Control Flow + Weight Sharing Examples Te

Learning PyTorch with Examples # Created On: Mar 24, 2017 | Last Updated: Sep 29, 2025 | Last Verified: Nov 05, 2024 Author : Justin Johnson Note This is one of our older PyTorch tutorials. You can view our latest beginner content in Learn the Basics . This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. At its core, PyTorch provides two main features: An n-dimensional Tensor, similar to numpy but can run on GPUs Automatic differentiation for building and training neural networks We will use a problem of fitting \(y=\sin(x)\) with a third order polynomi

Explore this link on the map →

saved by

related reading