Last midsummer I and a couple of my friends ended up having a quick chat about machine learning. I know basically nothing about the subject but proceeded to say something along the lines of "You could probably implement a simple neural network from scratch in python in ~100 lines of code.". Saying this was probably quite ignorant now that I think of it. This repository is meant for me to learn the basics of neural networks and build up increasingly complex nets from scratch.

1. hello_world.py is the simplest thing I could come up that could be classifie as machine learning. Extensively documented and this nice for learning purposes. (64 LOC which consists mostly of comments.)
2. gates_dumb.py is a single neuron model for AND and OR gates. Uses finite differences instead of derivates/gradients. (~100 LOC)
3. xor_dumb.py is similar to gates_dumb except that is uses a more complex model so that it can model the XOR gate. (~175 LOC)
4. xor_scalar.py is our first multi layer neural network using scalar framework. (~50 LOC)

1. scalar.py is a very inefficient neural network library + a way to visualize expressions via graphviz. (~200 LOC)

• XOR gate without scala framework?
• a simple Tensor framework.