I started the book yesterday. I already feel overwhelmed by the concept of tensors. For some reason the concept of "ranks" in tensors is not clicking with me. What is the difference between a rank-2 tensor and a 2 dimensional array? Is there any difference at all? Why am I so stupid.
We are in chapter 2 and implementing the
relu function. What does this mean. I don't understand it. What does it actually do. How does it actually transform the data we give it?
For the MNIST data we are working with, I don't understand how the images are turned into tensors (is that the right word?).
I'm on 2.3.2 Broadcasting and am just going to read over this and not mess with the code. Confused. Want to move on.
I hate how the book is like: "don't worry, we have simplified all the hard math stuff for beginners so you can understand it". And I don't understand it.
All the NumPy syntax is confusing to me, because I've never used NumPy before. The book seems to kind of assume you know NumPy.
I can't take all these fundamentals without trying to build something specific. I'm going to move on in the book.
Edited 1 month ago. Created 3 minutes ago.