linear-regression | PHP package for computation of simple linear regression | Testing library

I want to make a dataset using NumPy and then want to train and test a simple model like 'linear, or logistic`.

I am trying to learn Pytorch Lightning. I have found a tutorial that we can use the NumPy dataset and can use uniform distribution here. As a newcomer, I am not getting the full idea, how can I do that!

My code is given below

I'm trying to perform a Multiple Linear Regression with TensorFlow and confront the results with statsmodels library.

I generated two random variables X1 and X2 (so that anyone can reproduce it) that will explain the Y variable. The X2 variable is completely useless for this regression, it's just noise with a big scale so that the coefficient will result not significant (p-val close to 1). At the end I should obtain a model that is basically. y_data = alpha + (0.25)x1 + (0.00)x2 + error.

I tried to adapt this code to my randomly generated data, but unfortunately, this is not working at all. This below is my try:

The I() function in R is used to create new predictor in Linear Regression, such as X^2 for example:

I want to write a loop on R to perform linear regression on my dataset genes (= 210011 genes and 6 samples total; with columns the gene and the rows the samples) to identify how age and sex affect gene expression. I want to save the fitted value output from the linear regression in a new dataframe (generating basically a similar dataframe where on the columns there are the genes and in the rows the samples).

so the loop I wrote is:

My problem is for the mtcars data set in R, I need to create all possible additive linear regression models where I'm regressing on the mpg variable. The null model is easy, as there's

10 choose 0 ways to get the null model, and 10 choose 1 ways to create a SLR on mpg; 10 choose 2 ways to create a two variable regression on mpg; 10 choose 3 ways to create a SLR on mpg; etc.,

So in total, as this is equivalent to summing across the 10th row in Pascal's Triangle, the total models I need to consider comes out to be 1,024.

Now, the other tricky part is I need to somehow store each model in some separate object so that all the 2 variable models are grouped together, all the three variable models are grouped together, etc, on top of also storing all them together (though perhaps there's a more efficient way to do this). The reason for this is my task is to look at all of these models, take their AIC scores and their Mallow's Cp scores, store those in a data frame, and then sort those scores from lowest to highest and keep the top 10. On top of this, I need to also be able to store, see, and have access to/use the two best 1-variable models through the two best 10-variable models because I need to provide the r-squared values and adjusted r-squared values for these various models along with the error mean square value. I'm still pretty/relatively new to R/coding in general, but I provide my attempt below:

I am trying to run a regression in a loop with variables names changing at each loop. Similar to this setup here

At the end, I would like to save the fitted results in a list.

My code is the following:

I've recently been using tidymodels to run models and select parameters that best satisfy some objective function. For example using a hypothetical regression on mtcars data (using the regression examples from the bottom answer of this question as an example)

I am complete beginner to AI as well as tensorflow.js. Currently following the Machine Learning course of Stephen Grider. I should have got a output after the following code but instead i got error. Please help:

code: linear-regression.js:

I'm using this tutorial to perform a linear regression. Is there a way to add the summary (results) of the linear model to the plot in order to save it as a pdf.

This is the code: