### scheme | network:

Neural network
* Most neural networks accept bit patterns as input
  - are software simulations run on conventional computers
  - attempt to identify a single winning output neuron for a given set of inputs
  - can tolerate if one or more of the processing cells fail
  - suffer from the opaqueness of their learned associations.
  * It is modeled after the way the human brain works. Several computing cells work in parallel to produce a result. This is usually seen as one of the possible ways Artificial intelligence can work. Most neural networks can tolerate if one or more of the processing cells fail
* allow systems to learn and adapt to new material.
* architectures Neural networks viewed as directed signal flow graphs.
* are a class of adaptive algorithms
  - form of artificial intelligence
  - mathematical tool that can analyze large amounts of data from related markets
  - new discipline, and diagnostics, prognostics and control huge field
  - recent technology based on the way the human brain works
* are able to form generalizations and can identify patterns with noisy data sets
  - learn complex nonlinear mappings
  - adaptive systems that can react and adjust to changing conditions
* are artificial intelligence
  - learning devices inspired by the workings of the human brain
  - capable of learning patterns in input data
  - computational tools that mimic the way the human brain functions
  - computer programs modeled on the interaction of nerve cells in the human brain
* are computer systems that actually learn by doing
  - can recognize patterns and learn from experience
  - data driven models
  - different
  - excellent at detecting relationships in vast quantities of data
  - extremely useful in detecting underlying trends in data
  - good at pattern recognition, generalization, and trend prediction
  - inherently parallel computational models
  - machine learning
  - methods of pattern classification that are nonlinear and empirical
  - modeless systems that learn from the underlying relationships of data
  - more efficient than algebraic methods in dealing with big amounts of noisy data
  - one, of many, biologically influenced models for mathematics
  - part of nervous systems
  - poor at extrapolating far outside the training set
  - powerful non-linear models
  - programs designed to simulate the workings of the brain
  - self-learning systems using data of past behavior to predict future behavior
  - simply a mathematical tool
  - systems loosely based on the organisation of neurons in the brain
* are the most widely used form of artificial intelligence on the web
  - primary information processing structures used in neurocomputing
  - unique, defining characteristic of the nervous system
  - tools to be applied to problems
  - universal approximators
  - useful in artificial life because of their capability to learn
* are very capable of solving complex problems that chock other techniques
  - effective in the analysis and interpretation of complex signals
* assess the impact of a number of factors after a sequence of iv injections.
* attempt to function like a human brain
  - tease out meaningful patterns from vast amounts of data
* become more chaotic as dimension increases.
* bring objectivity and discipline to bear on previously ignored information.
* can also continuously learn, and can also be creative
  - analyze data and diagnose diseases
  - approximate nonlinear functions
* can be an intricate part of heightening a game's degree of difficulty
  - of considerable help in the visualization and analysis of multivariate data
  - process combinations of qualitative and quantitative data
  - solve entire classes of problems
  - yield accurate classifications of cases, even with many interrelated variables
* consist of layers of uniform processing elements, nodes, units, or neurons
  - many single units which are analogous to the brain s cells
* constitute a powerful tool for data mining.
* controls the hand movement.
* do what they are trained to do.
* enable the exploration of complex relationships that exist in corporate databases.
* have potential adaptability for agricultural appraisal analysis
  - practical applications and can also serve as models for human behavior
* have the ability to derive meaning from complicated or imprecise data
  - advantage that they can deal with noisy, inconsistent and incomplete data
  - capability of approximating any function
* imitate the structure of the biological nervous system.
* improve the overall model accuracy around setpoints where process data is available.
* improves the ability to predict risk even further.
* is another approach to artificial intelligence.
* learn how to solve problems
  - like a human does, by trial and error
  - new tasks much like people learn to throw a basketball through the hoop
* learning theory integrates behavior therapy and behavior genetics.
* mimic the nervous system.
* offer a method to evaluate differences between databases.
* perform communication switching tasks.
* provide a means to predict the effectiveness of care for a specific location
  - model of computation drastically different from traditional computers
  - an alternative to algorithmic methods
* represent a meaningfully different approach to using computers in the workplace
  - radically new departure in computer technology
  - words and people as mathematical vectors
* require a large amount of information.
* resemble the human brain because they can learn.
* restructure their knowledge base at each step in the learning process.
* sequence of neuron layers.
* tackle real-world problems.
* take a different approach to artificial intelligence
  - problem solving than that of conventional computers
* track spending patterns of both cardholders and typical fraud transactions.
* turn out to be a variation of the normal linear classifiers.
* use a digital computer to model the neural connections in human brains
  - fuzzy logic that is sometimes more statistical or probablistic than concrete
  - neural data
* work in an analogous way to the human brain
  - well in complex pattern recognition because they are very non-linear