Tuesday, 17 February 2015
Thursday, 8 January 2015
I1 and I2 are the inputs scaled to [-1,1] or [0, 1], depending on the activation function usedf()=Activation Function=Tanh(), Sigmoid() or any differential-able function
W=Current neurons input weights, initialized randomly between [-1, 1].
Wb=Bias Weight, connected to nothing, used as a threshold, initialized same as W
N=The output of the current neuron.
Markov Chains is a probabilistic process, that relies on the current state to predict the next state. For Markov chains to be effective the current state has to be dependent on the previous state in some way; For instance, from experience we know that if it looks cloudy outside, the next state we expect is rain. We can also say that when the rain starts to subside into cloudiness, the next state will most likely be sunny. Not every process has the Markov Property, such as the Lottery, this weeks winning numbers have no dependence to the previous weeks winning numbers.
The Bloom filter is a space efficient, probabilistic data structure, designed to test the membership of elements to a set. The trade-off for being a space efficient data structure is it may return false positives, but always returns definite negatives: Meaning Bloom filters can accurately test an element for non-membership to a set, but can only with probability test an element for membership. Bloom filters find application in circumstances where testing for non-membership saves resources such as calls to a web server, checking a proxy cache. Google uses Bloom filters in the Chrome browser as a preliminary check for malicious URL's.