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

NumPy arrays can also run on a GPU.

Which function can change the tensor in place?

Which function can change the tensor in place?

A tensor’s shape is (10,1,1,5), after calling squeeze(), what is the shape?

A tensor’s shape is (10,1,1,5), after calling squeeze(), what is the shape?

A tensor’s shape is (3,3)， after calling un-squeeze with dim=1, what is the shape now?

A tensor’s shape is (3,3)， after calling un-squeeze with dim=1, what is the shape now?

Which operation would expand one more dim for a tensor?

Which operation would expand one more dim for a tensor?

If you want to multiply a tensor of shape (10, 3, 4) with another tensor of shape (4, 6), which function should you choose?

If you want to multiply a tensor of shape (10, 3, 4) with another tensor of shape (4, 6), which function should you choose?

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Quiz