The phrase "a sequence of transformations" is correct and usable in written English.
It can be used in contexts such as mathematics, computer science, or any field that involves processes or changes over time.
Example: "In the study of algorithms, we often analyze a sequence of transformations that data undergoes to achieve the desired output."
Alternatives: "a series of changes" or "a chain of modifications".
Like the accounts of twentieth-century psychologists such as Maslow, Erikson, and Kohlberg, Hartley's theory describes a process of moral development as a sequence of transformations of the self.
It is shown that this rule set is complete, namely for any two equivalent circuits, S1andS2, there is a sequence of transformations, each of them in the rule set, which changesS1 to S2.
Mental mathematics, sometimes referred to as mental computation, is the act of performing calculations without the use of external tools through a sequence of transformations beyond counting or imagining the steps of the standard algorithm [24, 37, 46].
The Parry/Sullivan Theorem Two finite directed graphs are flow equivalent if and only if one can be gotten from the other by a sequence of transformations involving these six graph moves.
Following immunoreaction, the microarray data were processed by a sequence of transformations and then analyzed.
The creation of a ggplot involves a stepwise process that takes the defined component pieces, called layers, and coordinates them through a sequence of transformations to produce the final graph.
This leads to a sequence of transformation different from that obtained in binary alloys.
In a number of recent papers, a factory approach has been advocated in which one program after another is fed to an assembly line that consists of a sequence of transformation tools.
A sequence of spatial transformations may be generated at discrete points in time, or a single transformation may be generated as a continuous function of space and time.
Rash, for instance, could easily be modified to find a sequence of local transformations for an alignment, instead of trying to find a single rigid transformation (although a new training for Rash would be needed).
It works by representing a pipeline as a sequence of dataset transformations.
