@ -331,3 +331,17 @@ def dtconverter(o):
print(json.dumps(my_py_dict_var, default = dtconverter))
>> Neural networks are universal approximators - meaning that for any function F and error E, there exists some neural network (needing only a single hidden layer)
that can approximate F with error less than E.
>> Normalisation is required so that all the inputs are at a comparable range.
With two inputs (x1 and x2), where x1 values are from range 0 to 0.5 and x2 values are from range to 0 to 1000. When x1 is changing by 0.5, the change is 100%, and a
change of x2 by 0.5 is only 0.05%.