Machine Learning: Models with Learned Parameters
I strongly advise everybody with one day free (and not much better things to do) to implement a basic fully connected feedforward neural network (the classical stuff, basically), and give it a try against the MNIST handwritten digits database. It's a relatively simple project that learns you the basic. On top of that to understand the more complex stuff becomes more approachable. To me this is the parallel task to implement a basic interpreter in order to understand how higher level languages and compilers work. You don't need to write compilers normally, as you don't need to write your own AI stack, but it's the only path to fully understand the basics.<p>You'll see it learning to recognize the digits, you can print the digits that it misses and you'll see they are actually harder even for humans sometimes, or sometimes you'll see why it can't understand the digit while it's trivial for you (for instance it's an 8 but just the lower circle is so small).<p>Also back propagation is an algorithm which is simple to develop an intuition about. Even if you forget the details N years later the idea is one of the stuff you'll never forget.