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

# Math operation with scalar

If you have used the NumPy array before, you may already know that we can perform a math operation between a NumPy array and a scalar. PyTorch tensor supports almost the same operations. In PyTorch, we perform it in two ways:

- The operators, such as `+`, `-`, and `*`.
- The functions, such as `add`, `sub`, and `mul`.

```python
a = torch.tensor([1,2,3])
b = a + 3
b = a.add(3)
```

PyTorch supports most of the math functions under the native Python `math` module. You could find a complete list from the [official sites](https://pytorch.org/docs/stable/torch.html#pointwise-ops).

>**Notice**: These functions wouldn't change the original tensor, the changed tensor would be returned as a new tensor. PyTorch provides another version of these functions, which allows us to change the tensor **in-place**. These functions' names remain the same, except that the function names are suffixed with an `underscore`. Such as `add_`.

In this lesson, we will discuss element-wise mathematical operations between tensors and scalar values.

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Element-wise Mathematical Operations on Tensors

Math operation with scalar