Explain NEURAL NETWORK: Also, neural net or artificial neural network to distinguish it from the brain, upon which this algorithm is modeled. “A robust function that takes an arbitrary set of inputs and fits it to an arbitrary set of outputs that are binary... In practice, Neural Networks are used in deep learning research to match images to features and much more. What makes Neural Networks special is their use of a hidden layer of weighted functions called neurons, with which you can effectively build a network that maps a lot of other functions. Without a hidden layer of functions, Neural Networks would be just a set of simple weighted functions.”[kirk] See also deep learning, backpropagation, perceptron.
Different definitions in web development like neural network in Dictionary N.
- Manual NoSQL:
- Meaning management system that uses any of several alternatives to the relational, table-oriented model used by SQL databases. While this term originally meant “not SQL,” it has come to mean something closer neural network.
- Manual Naive Bayes Classifier:
- Meaning classification algorithms based on Bayes Theorem. It is not a single algorithm but a family of algorithms that all share a common principle, that every feature being classified is independent of the neural network.
- Manual N-Gram:
- Meaning sequences of n items (typically, words in natural language) to look for patterns. For example, trigram analysis examines three-word phrases in the input to look for patterns such as which pairs of neural network.
- Manual Null Hypothesis:
- Meaning model for a data set says that the value of x is affecting the value of y, then the null hypothesis—the model you're comparing your proposed model with to check whether x really is affecting neural network.
- Manual Normal Distribution:
- Meaning distribution. (Carl Friedrich Gauss was an early nineteenth-century German mathematician.) A probability distribution which, when graphed, is a symmetrical bell curve with the mean value at the neural network.