I’m learning the NLP course, and I saw in lesson 3 that Ngrams and Naive Bayes is the old-fashioned way.

Should I dig deeper to understand the algorithms clearly or just know the basics and going to the next lesson?

Thanks in advance.

Hi Hao

I think it is best to learn just what is on the course and then later learn about the other things.

For Bayes

The one thing I had to hunt down is Gamma(n+1) = n * (n-1) * (n-2)… so n!

Beta(a,b) = Gamma(a)*Gamma(b)/Gamma(a+b). This is used when you have no prior data.

Here are some articles

Regards Conwyn

you mean if the lesson told like SVD or Naive Bayes, I should search for more information about it and go to the next lesson ?

Hi Hao

No the opposite. Complete the course and then go and read about SVD and Bayes.

Sorry for my lack of clarity.

Regards Conwyn

Thanks a lot for your responses, so I’m ready for the next lesson now.

Hi Hao

It is worth doing the Computational Linear Algebra after NLP. It gives the background to SVD. It is mathematical and the audio is poor but I think if you do every single Fast.AI courses it helps with the overall understanding.

Regards Conwyn

Thanks for your advice. I really appreciate it.