Neural Networks
In simple words
Layers, weights and activations. The unit of construction for everything downstream.
The fuller explanation
A neural network is a stack of layers. Each layer multiplies its input by a matrix of learned weights, adds a bias, and passes the result through a non-linear function. That is genuinely all a layer is.
The power comes from composition. One layer can only draw a linear boundary. Two layers with a non-linearity between them can approximate essentially any continuous function, and depth beyond that buys efficiency: deep networks represent complex functions with far fewer parameters than shallow ones would need.
Three things are worth internalising early. The non-linearity is not optional - without it, stacked layers collapse algebraically into a single linear layer. The weights start random and are entirely learned; nothing about the structure encodes knowledge of your problem. And the width and depth you choose are hyperparameters, meaning you will find them by measurement rather than derivation.
Learn these first
Real prerequisites, taken from the map rather than guessed.
Sources
Where this came from, so you can go past us.
Where does this sit on your route?
The free assessment places you on the same map and names which terms stand between you and the role you want.
Take the free assessmentSee it in context
The Atlas shows this term with everything that leads into it and everything that follows, as one picture.
Open the map