Spoilers
Learn about the learning outcomes of this chapter.
We'll cover the following
What to expect from this chapter
In this chapter, we will:

Briefly review the steps of gradient descent (optional).

Use gradient descent to implement a linear regression in Numpy.

Create tensors in PyTorch (finally!).

Understand the difference between CPU and GPU tensors.

Understand PyTorch’s main feature, autograd, to perform automatic differentiation.

Visualize the dynamic computation graph.

Create a loss function.

Define an optimizer.

Implement our own model class.

Implement nested and sequential models using PyTorch’s layers.
Get handson with 1200+ tech skills courses.