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Double-loop learning. Double-loop learning entails the modification of goals or decision-making rules in the light of experience. The first loop uses the goals or decision-making rules, the second loop enables their modification, hence "double-loop". Double-loop learning recognises that the way a problem is defined and solved can be a source of ...
Loop unrolling. Loop unrolling, also known as loop unwinding, is a loop transformation technique that attempts to optimize a program's execution speed at the expense of its binary size, which is an approach known as space–time tradeoff. The transformation can be undertaken manually by the programmer or by an optimizing compiler.
Behavioral psychology and organizational development: In their 1978 work on organizational learning, Chris Argyris and Donald Schön developed the concepts of single-loop and double-loop learning. [22] Single-loop learning is the process in which a mistake is corrected by using a different strategy or method that is expected to yield a ...
Double-loop learning (see diagram below) is used when it is necessary to change the mental model on which a decision depends. Unlike single loops, this model includes a shift in understanding, from simple and static to broader and more dynamic, such as taking into account the changes in the surroundings and the need for expression changes in ...
Control flow. v. t. e. In computer science, control flow (or flow of control) is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. The emphasis on explicit control flow distinguishes an imperative programming language from a declarative programming language.
In stochastic (or "on-line") gradient descent, the true gradient of is approximated by a gradient at a single sample: As the algorithm sweeps through the training set, it performs the above update for each training sample. Several passes can be made over the training set until the algorithm converges.
How those governing variables are treated in designing actions are the key differences between single-loop and double-loop learning. When actions are designed to achieve the intended consequences and to suppress conflict about the governing variables, a single-loop learning cycle usually ensues.
Below is an example of a learning algorithm for a single-layer perceptron with a single output unit. For a single-layer perceptron with multiple output units, since the weights of one output unit are completely separate from all the others', the same algorithm can be run for each output unit.