This back propagation forward progagation business, even though was in serial modules, remained harry. Only 3 days of translation, half the time of the previous homework, but the later coding/debugging took awhile, prob cuz other classes got buzy too.

It’s just like a neural network: each module (node) inputs data, performs some simple tasks. But collectively the purpose and process seems very mysterious.

Challenges include understanding the purpose of propagating. Now I found it involves ‘filtering’ the input sample, weight vector, and weight vector gradient. Other challenges…reasoning between many functions/classes/variables, since it’s far from a linear command program, often tracing a variable’s lifetime change through multiple modules is same as fixing pipes, or intestines, from the inside. Some of the translated code template is never used, which confused me some time.

OOP can minimize code while forming process complexity. Naturally some processes, like altering and outputing variables, is useless. An object function is designed to output in two directions (like a train), but the unidirectional head and tail trains will always do wasted extra work. ==> good case of code readability vs efficiency. But readable doesn’t mean ‘easy to debug’!!

Any numerical input database works on this neural net (I don’t know which type is better suited tho…). Here professor gave the UCI isolet database, data describing single voiced alphabets. I’m quite amazed my best simulation score got 3.3% error (96.7% accuracy!). So it’s great for voice recognition. But real life has connected syllabus chains and semantic context dependent, so that’s another thing. Unless a preprocessor breaks an audio file into alphabet pieces, then neuralnet can aid in a larger context based algorithm.