This took longer than it should have. And though this is an already “won” race, I’m going to take a moment to enjoy this and celebrate by … moving on. While I did find the result I was looking for, it came in about around a mystery that I never solved.
• Machine Interaction, Neural Net, Psychology, Artificial Intelligence, and Finnegan
I’ll preface this with the fact that I am not a psychologist, I haven’t studied it since college. I’m not even a scientist. So what follows should be treated as nothing more than anecdotal. That being said, it seems fascinating, so if anyone out there feels the need to write a doctoral thesis on this please let me know how it turns out.
• Machine Learning, Neural Net, Python, MNIST, and Scikit-Image
A brand new rabbit hole of something to learn. And I’ll wisely choose to side-step this one for the moment, but it is interesting. How do you hold on to as much information as possible while downsizing an image to a size you want to work with?
• Machine Learning, Dropout, Regularization, AWS, GPU, Python, and Numpy
Neural Net Architecture is not a playground for those that demand instant gratification. The endless trials with slight variations of one parameter or another (made ever so much worse when you don’t take rigorous notes, head slap) provide feedback only when they are good and ready. So it is much like watching water boil. While we’re waiting. Lets break out Python’s cProfile and see what is going on under the hood, just to pass the time, of course.
• Neural Net, Vectorization, Linear Algebra, Python, and Machine Learning
So I failed to take notes. Lesson learned. It has been a hectic two weeks plying through a mess that I admittedly should not have made. I should have continued iterating through the design only with a test firmly in hand at each step. I didn’t and I paid the price. Here is where I should pile up some epic story of failure and perseverance, littered with trials and tribulations, successes and pains. But there is nothing so entertaining to be had in this venture, just a mess of spaghetti and a commit path just as wandering and even more useless. So I’ll just say it:
Project, check. Splash page for project, check (thank you, gh-pages). Documentation, in case someone want to use this thing, … Well I’ve got docstrings, that’s good enough, right? Not so, say they, what hold sway.
Initial foray into machine learning was an implementation of a basic Perceptron (image) on the Handwritten Digits sets from scikit-learn. The 8x8 pixel images in grayscale number 1797, and have been adjusted for skewing.