Neural Network Illustrated – Step by Step

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 function W=Current neurons input weights, initialized randomly between [-1, 1]. Wb=Bias Weight, connected to nothing, used as a threshold,...

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Markov Chains - Explained

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...

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Bloom Filters - Explained

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...

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