Tuesday 17 February 2015
Thursday 8 January 2015
Published January 08, 2015 by Tech-Effigy with 10 comments
I1 and I2 are the inputs scaled to [-1,1] or [0, 1], depending on the activation function used
f()=Activation Function=Tanh(), Sigmoid() or any differential-able functionW=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.
Published January 08, 2015 by Tech-Effigy with 18 comments
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.
Read More
Published January 08, 2015 by Tech-Effigy with 7 comments
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.
Read More
Subscribe to:
Posts (Atom)