This course teaches you how to create, manipulate, and understand tensors for machine learning using PyTorch.

Manipulate Tensor in PyTorch (Mini Course)_468 x 60.png

pytorch.tar.gz

pytorch

Tensors form the fundamental building block of the PyTorch ecosystem. Various components, such as network layers, loss function, gradient descent, and optimizer rely on underlying tensor operations.

In this course, you will get a comprehensive overview of tensors starting with how to create one. You will learn the many different types of tensors as well as how to concatenate and manipulate them by selecting elements, changing the shape, and more.

By the end of this course, you will have a basic understanding of how to work with PyTorch tensors and what they allow you to do.

Basic Pytorch Tensor Manipulation for Machine Learning

# Saving a single tensor

Sometimes, we want to dump a tensor to the disk for future use immediately after an operation. It could save a lot of time in scenarios where the processing takes too long and we don't want to go through the whole process again.

PyTorch provides `torch.save` to save objects to a file-like object. In this lesson, we only save the object to a file. The first parameter is the object we want to save, in this example, it's a tensor. In this example, the second parameter is a file path

```python
a = torch.tensor([1, 2, 3])
torch.save(a, "./output/tensor.pt")
```

In this lesson, we will look at dumping tensors to a local file and loading them again.


Saving and loading tensors

In this lesson, we will look at dumping tensors to a local file and loading them again.