Choice of Pipelines for Data Analysis

[WWGD]

TL;DR Summary

What kind of rules of thumb are there to decide choice of pipeline?

Hi,
So say I have some data to process. I am trying, say, Linear/Multilinear Regression. I know how to do this within Python Pandas. I can learn how with Tensorflow (TF). Would TF produce the same output given the "right" choice of Activation Functions *? Or would it output a model that is somehow "More General"?

* I assume this is the only/main variable affecting this choice and not other variables such as choice of metrics, sessions, etc.

 

[pbuk]

Given the same model structure (choice of metrics, properly normalized data, loss function) I don't see why TF should not converge on the same coefficients as a traditional calculation. However with a neural network we introduce hidden layers that create a non-linear fit which in most cases will perform better.

Have you worked through the Keras tutorial on the fuel efficiency dataset?

 

[WWGD]

Does TF stand for TensorFlow or The f$%*? ;). Thanks for your answer. Will look up the link; thanks.

 

[pbuk]

Does TF stand for TensorFlow or The f$%*? ;).

I must admit to having used the words "why won't you converge you f$%*?" or similar on a number of occasions.

 

[jedishrfu]

Theres a couple of books that may help here:

Hands On ML with Scikit-Learn, TF, and Keras

https://www.amazon.com/dp/1492032646/?tag=pfamazon01-20

with some later chapters for pipelines and hands-on project setup.

and the 100 pg ML book:

https://www.amazon.com/dp/199957950X/?tag=pfamazon01-20