This artificial intelligence wiki is a beginner’s guide to machine learning and deep learning. The goal is to give readers an intuition for how algorithms work, with calculations and code you can follow.
Getting Started
- Neural Networks and Deep Learning. Follow a crop-yield prediction through two hidden layers and see how a training step changes the answer.
- Datasets and Machine Learning. Choose examples that fit your problem, then learn why training, validation and test sets have different jobs.
- LSTMs and Recurrent Neural Networks. Trace a character prediction through two steps and see how memory carries information through a sequence.
For the broader vocabulary, read our comparison of artificial intelligence, machine learning and deep learning.
The line between mathematics and philosophy is blurry when we talk about artificial intelligence, because with AI, we ask the mineral called silicon to perceive and to think, actions once thought exclusive to meat, and now possible with computation.