Find out what is CV, ML and how to implement those things!
Finished:
/SVM&HOG SVC + HOG (feature detection)
Staged:
/KNC (k-Neighbors Classifiers)
/SVM-old SVM-related
/SVC Support Vector Classifiers ('rbf' kernel as default)
Data set:
/MNIST MNIST (ungzipped)
Requirements:
~~Approx. 600MB hard drive (KNC is memory-hogging)~~
A bunch of RAM
The Anaconda Software Distribution (or classic Python3 + resolve-dependencies-yourself combo)
python-mnist (stuff that handles MNIST the right way(?))
How 2 Do Project
[o] Step 0. What the hell is that; an image
[x] Step 1. Where the hell are the numbers
[x] Step 2. What the hell do those numbers mean
[x] Step 3. How the hell do I tell you that I know what the hell those numbers mean
x = OK; o = Partial; None = None
Note: OK != Perfect/Flawless/Completely functional