A neuralnet is a graph of neurons which usually but not necessary come with multiple layers for feeding forward and backward. Neuralnet is also a function, the whole network is a function.
Why Neuralnet can Learn
A neuralnet is a graph of neurons with different types (different types of neurons) of calculation inside. The point is giving output with a certain input. In order to learn different cases of inputs, the params inside the network changes to adapt and give different results as outputs.
The param adaption process is called weight update, and it is the learning process.
Neuralnet can learn a huge number of cases because the combination of neurons thru’ connections between layers make the exponential amount of combinations.
Generalisation
Neuralnet can group up inputs into a class, this is called generalisation.
Summarisation
Neuralnet condense, reduce the amount of data from input to output and very small number of numerical values in output, this is called summarisation.
Fuzzy Logic Output
Neuralnet (except utilising perceptrons) never gives exact output, it is always a fuzzy result, eg. value probability of a class between 0 and 1, this is fuzzy logic output.
Learning Theories
The main 2 learning theories used in neural networks are Cauchy Gradient Descent (used in supervised learning) and Bellman Equation (used in reinforcement learning). Reinforcement learning can have supervised learning inside too.
Data Engineering
Before the data can be fed to the network, it needs a certain level of processing called data engineering. Data engineering is regular programming and logics to convert data.
