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The perils of constant feedback

Absorbing Feedback

The Gradient Descent, which is a machine learning technique used in a neural network, provides us with this insight:

  • If we take absorb too much feedback for our actions towards our goal, we will move extremely slow and can fall back or settle for some mediocre solution.
  • If we ignore all feedback and just act, we may probably never reach our goal.

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    IDEA EXTRACTED FROM:

    The perils of constant feedback

    The perils of constant feedback

    https://tjcx.me/posts/feedback-learning-rate/

    tjcx.me

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    Key Ideas

    The App Notification Analogy

    When we observe App notifications, there is a correlation between the number of notifications sent by the App and the probability that the user opens it.

    If the number of notifications is high, there is a greater chance of the user opening the app, while at the same time, a greater risk of annoying the user. This analogy plays the same in the area of the feedback that we receive for our actions.

    Absorbing Feedback

    The Gradient Descent, which is a machine learning technique used in a neural network, provides us with this insight:

    • If we take absorb too much feedback for our actions towards our goal, we will move extremely slow and can fall back or settle for some mediocre solution.
    • If we ignore all feedback and just act, we may probably never reach our goal.

      Changing Your Feedback Frequency

      The way to overcome the problem of "Feedback Frequency" is to change your 'Step Size', which is inversely proportional to the feedback received. This is done by:

      • Slowly Decreasing Step Size: Take less feedback in the beginning but increasing it as you move closer towards your goal.
      • Cycling between small and big Step Size: Taking less and more feedback in intervals.
      • Increasing the momentum of Step Size: Taking more feedback in the beginning and as you learn, decrease the amount of feedback and move forward faster, using the momentum.

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