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

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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

## Concatenate the tensors

`torch.cat()` can concatenate a sequence of tensors in a given dimension. All tensors should have the same shape. Below are the important parameters.

- `tensors`: A list of tensors must have the same shape.
- `dim`: The dimension over which the tensors are concatenated. Take 2D tensors as an example, `dim=0` means the operation would be performed row-wise. `dim=1` means the operation would be performed column-wise.


# Concatenate the tensors

`torch.cat()` can concatenate a sequence of tensors in a given dimension. All tensors should have the same shape. Below are the important parameters.

- `tensors`: A list of tensors must have the same shape.
- `dim`: The dimension over which the tensors are concatenated. Take 2D tensors as an example, `dim=0` means the operation would be performed row-wise. `dim=1` means the operation would be performed column-wise.


In this lesson, we would show how to concatenate tensors.

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Concatenating tensors

Concatenate the tensors