Feedback loop: Difference between revisions

Latest revision as of 20:37, 17 March 2023

See also: Machine learning terms

Introduction

A feedback loop is a systemic mechanism in which an input is processed and an output produced. This output then serves as input for subsequent iterations of the process, creating an endless cycle. A hallmark feature of such a feedback loop is how one iteration's output influences subsequent ones - creating self-regulating cycles.

Feedback loops can be either positive or negative. In a positive feedback loop, each iteration's output reinforces that of its previous iteration and leads to exponential growth or decay. On the other hand, in a negative feedback loop, each iteration dampens its input for future iterations, leading to stability or convergence.

Feedback loops are ubiquitous in both natural and artificial systems, such as ecology, economics, and engineering. In machine learning applications, feedback loops help to enhance models' performance over time by providing valuable inputs for improvement.

Feedback loop in machine learning

In machine learning, feeback loop occurs when the model's predictions influence the training data for the same or other models. As an example, let's imagine YouTube recommendation models influencing the videos people watch which will influence the video future recommendation models.

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 A [[feedback loop]] is a systemic mechanism in which an [[input]] is processed and an [[output]] produced. This output then serves as input for subsequent iterations of the process, creating an endless cycle. A hallmark feature of such a feedback loop is how one iteration's output influences subsequent ones - creating self-regulating cycles.
-Feedback loops can be either positive or negative. In a positive feedback loop, each iteration's output reinforces that of its previous iteration and leads to exponential growth or decay. On the other hand, in a negative feedback loop, each iteration dampens its input for future iterations, leading to stability or convergence.
+Feedback loops can be either positive or negative. In a positive feedback loop, each iteration's output reinforces that of its previous iteration and leads to exponential growth or decay. On the other hand, in a negative feedback loop, each iteration dampens its input for future iterations, leading to [[stability]] or convergence.
 Feedback loops are ubiquitous in both natural and artificial systems, such as ecology, economics, and engineering. In machine learning applications, feedback loops help to enhance models' performance over time by providing valuable inputs for improvement.
-==Feedback loops in machine learning==
+==Feedback loop in machine learning==
-Machine learning relies on feedback loops to iteratively train models. The idea behind this technique is that by using the output of one iteration, parameters in the model can be tweaked so that subsequent iterations produce improved outcomes.
+In [[machine learning]], [[feeback loop]] occurs when the [[model]]'s predictions influence the [[training data]] for the same or other models. As an example, let's imagine [[YouTube]] [[recommendation]] models influencing the videos people watch which will influence the video future recommendation models.
-Machine learning algorithms typically employ error feedback loops. In this type of loop, the model is trained using a dataset of input-output pairs where the desired output is known for each input. The difference between that desired output and what actually appears onscreen is known as an error, and it's used to alter model parameters accordingly.
-The basic elements of an error feedback loop are as follows:
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]
-. The model takes an input and produces an output.
-. The error between the desired output and actual output is calculated.
-. This error serves to update the model parameters.
-. Repeat these steps 1-3 for all input-output pairs in the training dataset.
-Once a model has been trained using the training dataset, it can be tested against another set of data to assess its performance. If the model performs well on this test set, it can then be applied to making predictions about unobserved information.
-==Why are feedback loops important in machine learning?==
-Feedback loops are essential in machine learning for several reasons. Firstly, they allow models to learn from their errors and enhance their performance over time - especially important in applications with dynamic data such as natural language processing or image recognition.
-Second, feedback loops enable models to adjust to changing circumstances and handle unexpected inputs. This is essential in applications where the model may come across inputs that differ from what it was trained on.
-Finally, feedback loops enable models to optimize their performance based on an objective such as minimizing error or maximising accuracy. This is essential in applications where model accuracy is essential, such as medical diagnosis or financial forecasting.
-==Explain Like I'm 5 (ELI5)==
-A feedback loop is like playing a game where you get better each time. When you make mistakes, you learn from them and try again. Computers learn in much the same way - they take in information, make predictions, then learn from their errors to make even better ones in future attempts. This helps them become increasingly adept at tasks such as recognizing pictures or understanding language.
-==Explain Like I'm 5 (ELI5)==
-Imagine teaching a robot how to play catch with a ball.
-Throw the ball to your robot, and it attempts to catch it. If successful, give them a "good job" and maybe some treats as rewards.
-But if the robot misses the ball, you can simply say "oops!" and instruct it how to adjust its movements for improved success in future attempts.
-This works like a feedback loop in machine learning. The robot attempts something, and based on its results it receives feedback on whether it did it correctly or incorrectly. Afterward, it uses this feedback to adjust what it does next time around.
-Just like when you learn how to ride a bicycle or draw a picture, the robot gets better with practice and feedback.
-[[Category:Terms]] [[Category:Machine learning terms]]