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Posted 06-06-2024 at 06:18 AM by rizitis
Updated 06-06-2024 at 06:20 AM by rizitis

I was watching MIT Introduction to Deep Learning | 6.S191
Al thought I cant understand everything because I have no studies and I m not native English spoken... I inspired Triadic-Optimization by connecting in my mind what video suggest for model training and the ancient Greek mathematician Aristarchus.
In theory its an implements a modern approach to simplifying and optimizing processes, with a focus on AI training. Drawing from Aristarchus's "Simple Method of Three," aim to streamline complex methodologies into efficient and effective solutions for AI model development and training.

In machine learning, understanding the relationship between the predictions of a model and the underlying data is crucial for effective model development and interpretation. One interesting relationship to explore is the connection between the average of the data points and the predictions made by the model.
Consider the following mathematical expression:

This equation represents the average of an infinite number of data points ( y_i ), denoted by ( \mu ). Here, ( n ) represents the number of data points, and ( y_i ) represents each individual data point.
Interestingly, this concept can be linked to the predictions made by a machine learning model. If a model is trained to predict values based on certain features ( x_1, x_2, ..., x_m ), it can be represented as:

In this equation, ( y ) represents the predicted value, ( w_0 ) is the bias term, and ( w_1, w_2, ..., w_m ) are the weights corresponding to the features ( x_1, x_2, ..., x_m ).

Now, let's connect the average concept with the model predictions. If a model is trained effectively, it should ideally produce predictions that are close to the average of the data points. In other words, the average of the predicted values should approach the true average of the data.

By understanding this relationship, we can gain insights into how well a model captures the underlying patterns in the data. If the model's predictions deviate significantly from the average of the data, it may indicate issues with the model's performance or the need for further investigation into the data.

In conclusion, exploring the relationship between the average of the data and the predictions made by a model can provide valuable insights into the effectiveness and reliability of the model in capturing the underlying data distribution.

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