When the package is imported with the keyword "using", it is not necessary to use the Package.function syntax with the functions exported by the package. For example: Plosts.scatter(x, y) It can be written this way: scatter(x, y)
Hi Elias, I use the dot notation to help new Julia programmers know where the imported functions are coming from. But you are right! Maybe next episode I should mention that the dot notation is not necessary and that I use it for this specific reason.
@@DrRandyDavila Now I get it, it's just to simplify for beginners. I also think it's important to make that detail clear in the next episode. Beginners may be confused as Julia code is not normally written like this.
I think Stochastic Gradient Descent was implemented incorrectly. We had: data = [(x_train, y_train)] # a list containing 1 tuple Later we had: N = length(data) # so N = 1 And in the loop, for each epoch we have: i = rand(1:N) # so rand(1:1) which is always 1
Same feeling i am learning m.l theory and somehow flux/julia syntax mathches the intution . Am never going back to python unless ther's a package Julia doesnt have..!
OMG! is the pluto output sane now? does it show below the cells like 100% of the rest of the world’s agreed upon standard for command output? I’m gonna cry!
![[05x02] Linear Regression | Regression | Supervised Learning | Machine Learning [Julia]](/img/n.gif)
Great content, especially for beginners like me entering the Julia world. Very excited to work in Julia for Machine learning tasks. Thank you !
When the package is imported with the keyword "using", it is not necessary to use the Package.function syntax with the functions exported by the package.
For example:
Plosts.scatter(x, y)
It can be written this way:
scatter(x, y)
Hi Elias, I use the dot notation to help new Julia programmers know where the imported functions are coming from. But you are right! Maybe next episode I should mention that the dot notation is not necessary and that I use it for this specific reason.
@@DrRandyDavila Now I get it, it's just to simplify for beginners.
I also think it's important to make that detail clear in the next episode.
Beginners may be confused as Julia code is not normally written like this.
I think Stochastic Gradient Descent was implemented incorrectly.
We had:
data = [(x_train, y_train)] # a list containing 1 tuple
Later we had:
N = length(data) # so N = 1
And in the loop, for each epoch we have:
i = rand(1:N) # so rand(1:1) which is always 1
Hi L W, Yes, very good catch! This is something we will address in next weeks episode. Thank you again for pointing this out!
Same feeling i am learning m.l theory and somehow flux/julia syntax mathches the intution . Am never going back to python unless ther's a package Julia doesnt have..!
OMG! is the pluto output sane now? does it show below the cells like 100% of the rest of the world’s agreed upon standard for command output? I’m gonna cry!