In my two previous posts, we saw how we can perform Linear Regression using TensorFlow, but Iāve used Linear Least Squares Regression and Cholesky Decomposition, both them use matrices to resolve regression, and TensorFlow isnāt a requisite for this, but you can use more general packages like NumPy. One of...

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## Cholesky Decomposition for Linear Regression withĀ TensorFlow

Several years have already passed since the onset of the Deep Learning boom. I have witnessed impressive achievements like ChatGPT and Midjourney, however, I am still amazed at how traditional methods like Cholesky decomposition remain extremely useful and efficient. Particularly, for tasks like Linear Regression, this method stands out due...

Continue reading...## Linear Least Squares Regression withĀ TensorFlow

Linear Least Squares Regression is by far the most widely used regression method, and it is suitable for most cases when data behavior is linear. By definition, a line is defined by the following equation: For all data points (xi, yi) we have to minimize the sum of the squared...

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