Linear Regression in Numpy
Explore how to implement a linear regression model step-by-step using Numpy and gradient descent. This lesson helps you understand core steps such as parameter initialization, forward pass, loss calculation, gradient computation, and parameter updates. By the end, you'll appreciate the role of PyTorch by first experiencing the challenges of manual implementation.
We'll cover the following...
Implementing linear regression using Numpy
It is time to implement our linear regression model using gradient descent and Numpy only.
“Wait a minute. I thought this course was about PyTorch!” Yes, it is, but this serves two purposes. First, it introduces the structure of our task, which will remain largely the same. Second, to show you the main pain points. Now, you can fully appreciate how much PyTorch makes your life easier.
For training a model, there is an initialization step first (line numbers refer to the code ...