Advanced operations in PyTorch

After exploring basic tensor operations, let’s now dive into more advanced operations in PyTorch, specifically the linear algebra operations that form the backbone of most numerical computations in deep learning.

Getting ready

Linear algebra is a subset of mathematics. It deals with vectors, vector spaces, and linear transformations between these spaces, such as rotations, scaling, and shearing. In the context of deep learning, we deal with high-dimensional vectors (tensors), and operations on these vectors play a crucial role in the internal workings of models.

How to do it…

Let’s start by revisiting the tensors we created in the previous section:

print(t1)
print(t2)

The dot product of two vectors is a scalar that measures the vectors’ direction and magnitude. In PyTorch, we can calculate the dot product of two 1D tensors using the torch.dot() function:

dot_product = torch.dot(t1, t3)
print(dot_product...