In this article we’ll form a thorough understanding of the neural network, a cornerstone technology underpinning virtually all cutting edge AI systems. We’ll first explore neurons in the human brain, and then explore how they formed the fundamental inspiration for neural networks in AI. We’ll then explore back-propagation, the algorithm used to train neural networks to do cool stuff. Finally, after forging a thorough conceptual understanding, we’ll implement a Neural Network ourselves from scratch and train it to solve a toy problem.
Who is this useful for? Anyone who wants to form a complete understanding of the state of the art of AI.
How advanced is this post? This article is designed to be accessible to beginners, and also contains thorough information which may serve as a useful refresher for more experienced readers.
Pre-requisites: None
Neural networks take direct inspiration from the human brain, which is made up of billions of incredibly complex cells called neurons.
