3.4, due on October 5

Difficult:
The most difficult part of today's reading was the proof of Proposition 3.4.7. The steps made sense, but I had a hard time understanding the setup.

Reflective:
Okay, I have a real question about this section. If we can build a priority queue in O(n) time, and we pop things off a priority queue in O(n) time, does that mean we have an O(n) sorting algorithm? That's what it seems like! But as far as I know, that isn't the case.

Comments

Post a Comment

Popular posts from this blog

8.7, due December 6

Difficult: The hard part of this section for me was understanding formula 8.48 and the Vector Space Structure section -- specifically the part about scaling relations. Reflective: I can see how these kinds of functions could be better for approximating functions with local behavior, whereas to express such functions in a Fourier basis would be very computationally expensive. I can also see how the simplicity of the function could cause it to be easier to work with (differentiate, evaluate, etc.)
Read more

8.5, due Dec 3

Difficult: I understood the mathematical concept of a convolution. And I understand how convolutional neural networks work like the back of my hand. But after reading this section, I still don't understand how the two mesh together. I was on board with the description given in the intro to 8.5, until they barely mentioned filters and feature extraction again for the rest of the section. Ex. 8.5.11 helped, but more examples like that in class would make a big difference. Reflective: I enjoyed what I did understand of Ex. 8.5.11, which explained that you could apply a convolution to a set of samples to remove some frequencies while leaving others untouched. I still missed the "How", though.
Read more

8.6, due Dec 5

Difficult: This section was actually pretty reasonable. The only part I had trouble with was understanding their method of antialiasing. I don't understand exactly how you can remove all Fourier coefficients outside a certain interval. Reflective: I really liked their examples of aliasing -- the spinning rotor blades, and even the plotting of functions. That was creative but very applicable example.
Read more