about customizing your PyTorch image. For more information about PyTorch, including tutorials, documentation, and examples, see: ‣ PyTorch website ‣ PyTorch project This document provides information Guide. ‣ For non-DGX users, see NVIDIA ® GPU Cloud ™ (NGC) container registry installation documentation based on your platform. ‣ Ensure that you have access and can log in to the NGC container registry NVIDIA_VISIBLE_DEVICES environment variable). For more information, refer to the nvidia-docker documentation. Note: Starting in Docker 19.03, complete the steps below. The method implemented in your system

torch.float torch.FloatTensor 64-bit integer (signed) torch.long torch.LongTensor see official documentation for more information on data types. ● Using different data types for model and data will cause shape x.dtype x.dtype ref: https://github.com/wkentaro/pytorch-for-numpy-users see official documentation for more information on data types. Tensors – PyTorch v.s. NumPy ● Many functions have the same gradients of prediction loss. 3. Call optimizer.step() to adjust model parameters. See official documentation for more optimization algorithms. Training & Testing Neural Networks – in Pytorch Define Neural

__version__)' python -c 'import torch; print(torch.cuda.is_available())' Useful Links Miniconda Documentation PyTorch: Getting Started Install TensorFlow

https://oncomputingwell.princeton.edu/2018/05/jupyter-on-the-cluster/ • Best reference is PyTorch Documentation • https://pytorch.org/ and https://github.com/pytorch/pytorch • Good Blogs: (with examples and

在确认这是你想要的之后，执行以下命令： git add chapter_appendix_tools/how-to-contribute.md git commit -m 'fix typo in git documentation' git push 然后，更改后的代码将位于存储库的个人分支中。要请求添加更改，你必须为本书的官方存储库创建一个Pull请 求。 提交Pull请求 如 图16.5.6所示，进入gihub上的存储库分支，选择“New