2023年12月11日 (一) 04:36的版本

Installation

Conda Installation

conda create --name deeplearning python=3.11
conda activate deeplearning
python --version
// 3.11.5

Install pytorch

conda install pytorch::pytorch torchvision torchaudio -c pytorch

To verify:

import torch
x = torch.rand(5, 3)
print(x)

Output:

tensor([[0.2162, 0.2653, 0.6725],
        [0.5371, 0.4180, 0.1353],
        [0.3697, 0.5238, 0.0332],
        [0.6179, 0.5008, 0.9435],
        [0.1182, 0.3233, 0.9071]])

Concepts

Tensor

Tensors are a specialized data structure that are very similar to arrays and matrices. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters.

Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators.

@@ 第1行： / 第1行： @@
+= Installation =
 == Conda Installation==
@@ 第30行： / 第31行： @@
          [0.1182, 0.3233, 0.9071]])
 </syntaxhighlight>
+= Concepts =
+== Tensor ==
+Tensors are a specialized data structure that are very similar to arrays and matrices. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters.
+Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators.
 [[Category:Deep Learning]]
 [[Category:PyTorch]]