Would be glad to receive your response on,
“how the neuron definition are self created (as implied in the video) given a set of inputs A & set of outputs B? Does the system sort of run a regression analysis to assume best fitting functions where in at each level of neuron it can only take certain logical values only(Then the system must try all these possible values one by one to see which best fits the overall equation for the given value of inputs and outputs and finalize the ones that offer these best fits).”
Best
Pankaj
Each type of problem (i.e. predicting a real value, or predicting a classification) has a different standard model. One is “linear regression”, the other is “logistic regression”.
There is a weight value associated with each “activation unit” (i.e. ‘neuron’).
When designing the model, you can vary the number of units, and how they are interconnected.
A relatively simple mathematical process is used to adjust the weight values so that the model’s predictions matches the known values (using a set of labeled data for training) as closely as possible.
Hmmm, may not be as fancy as what I imagined it to be, nonetheless your response doesn’t read any less fascinating.
Where do I go and learn more about this?
The way you have interpreted it for me, I feel that it would render itself well in a graphical representation. Is there a graphical rendition as well for this explanation available anywhere?