The Neural Network
The neural network is an essential part of machine learning that mimics the biology of the human brain. Artificial neurons are technological components made of sets of weighted inputs, and functional infrastructure that fires based on those weighted inputs. This is very similar to the ways that individual neurons in the brain work to send electrical impulses through the brain to interpret sensory data.
In machine learning projects, you typically have a neural network with an input layer, hidden layers and a corresponding output layer. Data filters its way through the neuron layers and produces extremely sophisticated results – these results are based on probabilities, not purely deterministic programming, as mentioned above. In other words, instead of just codebases that work on linear iterations, machine learning utilizes this artificial neural structure to do more with big data.
Learning more about neural networks gets you much deeper into machine learning and deep learning – for instance, listening to Marvin Minsky discuss how neural networks are like advanced logic gates shows how artificial intelligence has built on the technologies that came before it. Understanding the artificial neuron also helps you to figure out more about how the structure of machine learning programs work.
This video from 3Blue1Brown talks about how a neural network can simulate the work of the brain’s visual cortex – and relates the mathematical equations often used in algorithm research to the patterns of layers of neurons to show, for example, how neural networks process things like handwriting.