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constraint satisfaction problemsfurthermore i was perhaps most proud of ai on a sunday however this particular sunday a friend of mine found an
appropriate problems for ann learningconversely as we did for decision trees there its important to know where anns are the right representation
overfitted the datamoreover notice that as time permitting it is worth giving the training algorithm the benefit of the doubt as more as possible
local minima - sigmoid unitsalternatively in addition to getting over some local minima where the gradient is constant in one direction or adding
adding momentum - sigmoid unitshowever imagine a ball rolling down a hill as it does so then it gains momentum in which its speed increases and it
backpropagationhowever backpropagation can be seen as utilising searching a space of network configurations as weights in order to find a
backpropagation learning routineconversely as with perceptrons there the information in the network is stored in the weights than the learning
solution of multi-layer ann with sigmoid unitsassume here that we input the values 10 30 20 with the three input units and from top to bottom so
learning abilities of perceptronsconversely computational learning theory is the study of what concepts particular learning schemes as representation
learning algorithm for multi-layered networksfurthermore details we see that if s is too high the contribution from wi xi is reduced it means
perceptron traininghere the weights are initially assigned randomly and training examples are needed one after another to tweak the weights in the
units of artificial neural networkshowever the input units simply output the value that was input to them from the example to be propagated so every
perceptronshowever the weights in any ann are usually just real numbers and the learning problem boils down to choosing the best value for each
architecture of artificial neural networkspresumably artificial neural networks consist of a number of units that are mini calculation devices but
artificial neural networkshowever imagine now in this example as the inputs to our function were arrays of pixels and there actually taken from
ann representationmostly anns are taught on ai courses since their motivation from brain studies and the fact which they are used in an ai task and
id3 algorithmfurther for the calculation for information gain is the most difficult part of this algorithm hence id3 performs a search whereby the
basic ideahowever in the above decision of tree which it is significant that there the parents visiting node came on the top of the tree whether we
specifying the problemnow next here furtherly we now use to look at how you mentally constructed your decision tree where deciding what to do at the
reading decision treeshowever we can justified by see that a link between decision tree representations and logical representations that can be
decision tree learningfurthermore there is specified in the last lecture such as the representation scheme we choose to represent our learned
variable or compound expression - unification algorithmhere some things to note regarding this method are i there if really trying to match a
function name or connective symbolwhether if we write opx to signify the symbol of the compound operator then predicate name and function name or
unification algorithmhere if notice for instance that to unify two sentences as we must find a substitution that makes the two sentences the same
example of unificationlet now here assume instead that we had these two sentences as knowsjohnx rarr hatesjohn x knowsjack mary thus here