Get familiar with the basics of the Pytorch Tensor framework.

Mastering Image Classification

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 module, we will get a comprehensive overview of tensors, starting with how to create one. We 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 module, we will have a basic understanding of how to work with PyTorch tensors and what they allow us 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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