Hi Team,

In the attached screenshot, I have two queries:-

a. Why we’re using mean to calculate accuracy? Accuracy is no. of correct predictions divided by total no. of predictions but I’m not getting why we’re taking mean here.

return (pred == y).float().mean()

b. Why we’re dividing weights by sqrt of 2 here? And what does requires_grad() function will do with the weights?

weights1 = torch.randn(2, 2) / math.sqrt(2)

weights2 = torch.randn(2, 4) / math.sqrt(2)

In the video, i’m not able to understand the explaination given. So, please provide a detailed explaination.

Thanks

Shweta