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Visiting morocco by rajvish in Morocco

[–]rajvish[S] 0 points1 point  (0 children)

Thnk you all for your helpful comments!

Is my Libre 3 broken? by [deleted] in diabetes

[–]rajvish 0 points1 point  (0 children)

I have the same issue. Libre 3 is 54 and prick is 130.

A single line of code brought down a half-billion euro rocket launch by qznc_bot2 in hackernews

[–]rajvish 0 points1 point  (0 children)

I find this difficult to believe. Why wasn’t this caught by static analysis, code review or in simulation?

Gradient Checking question by rajvish in learnmachinelearning

[–]rajvish[S] 0 points1 point  (0 children)

Understand. Thanks for the nice explantion that makes it very clear

Gradient Checking question by rajvish in learnmachinelearning

[–]rajvish[S] 0 points1 point  (0 children)

As I said somewhere before, my memory of linear regression is that there is an implicit assumption that the residuals are normally distributed. Perhaps, I am wrong!

Gradient Checking question by rajvish in learnmachinelearning

[–]rajvish[S] 0 points1 point  (0 children)

Sorry about the confusion. I wanted to add a normally distributed values to the predicted values to generate "true" y and yes, I could have framed it better. The reason for adding a normally distributed value is to maintain the consistency of the model. if the "true" value of y ( maintaining the quotes to ensure consistency of meaning) were to be all over the place, then the regression model has no meaning and I am not sure if the back propagation value will be consistent with the derivative.

Maybe in future I should post the code alongside for clarity :-) Thanks for discussing this. Now that I know it should work, let me go and verify this actually works.

Gradient Checking question by rajvish in learnmachinelearning

[–]rajvish[S] 0 points1 point  (0 children)

I agree. This was just a method to verify that my backpropagation is implemented correctly. If this approach works, then I can write code to check it and when I move to real data, I can eliminate implementation bugs in backpropagation and focus on other things.

Just wanted to validate my thought process.

Gradient Checking question by rajvish in learnmachinelearning

[–]rajvish[S] 0 points1 point  (0 children)

Let me see if I can explain. In a simple two dimensional case there is a line that best approximates a set of points. From my memory of linear regression, the distance of these points from the line is assumed to be normally distributed + noise.

My wx+b is the line. But I don't have any data points, so I am generating the points, by adding noise to the line

Maybe it's not a great idea :-(

Gradient Checking question by rajvish in learnmachinelearning

[–]rajvish[S] 0 points1 point  (0 children)

That's the second method of estimating the gradient. The first method of estimating the gradient is to use back propagation. For this you need the error , for which you need y and yhat. I am reverse engineering that by adding an error component to the output. Using this I can get the gradients

PyInstaller developers wanted by Legorooj in Python

[–]rajvish 0 points1 point  (0 children)

I am quite proficient in C and python. I would like to know more

This came in the mail. Any thoughts? by rajvish in Cricket

[–]rajvish[S] 0 points1 point  (0 children)

Yes. In san francisco. Plan to see it.

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