Video

Bro is rapping

Where can I download the files?

little time ago, I was working with rmsd from StatsBase, I did find something annoying: rmsd(a::AbstractArray{T}, b::AbstractArray{T}; normalize) where T<:Number @ StatsBase see this: `StatsBase.rmsd([2,3,5],[2.1,3.2,5.1])` Int64 and Float64 are subtypes of Number, but this asks for the same subtype. When you define T for both arg, one should be aware that both arguments have to get the same subtype. I asked them to change this adding S<:Number for one of the arguments so we can have a mix of Number subtypes, but probably they have some restrictions in other part of the packages I guess.

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Great work, maybe a video on modules, imports, exports and tips to avoid module pollution would be a good idea :)

Really needed subject thanks!

I got lost.....too quick.

Please make Julia tutorials of web developpment, thanks for your videos on Julia.

Thank you for this subject! Nice animation/transition btw Always good quality content ❤

Great vid, Julia needs more material like this in my opinion! I was also wondering which theme you are using, since the default vscode ones don't treat julia tokens correctly...

Cool stuff! What do you use to create the animations? Can you share the source code?

No fancy transitions, no music, just the information I needed: thank you very much for this tutorial!

What is the ggplot2 is Julia?

Nice video!

Julia is awesome! However, it seems to have a super-steep learning curve before one comes to master the ins-and-outs of it to programme with full confidence and agility. Python seems to have 'tons and tons' of YT videos just on the basics -- so very helpful for the struggling and fumbling beginners thereat. Thus, a very humble request from you: Can you pls take some time out of your busy schedule to come up with 'tons and tons', so to speak, of Julia-centric YT videos so that the wannabe absolute beginners can successfully overcome the steep learning curves, to tackle the biggies' demands of taking advantage of the sheer POWER of Julia and its efficiency plus effectiveness! It will go a very long way for succeeding generations of Julia enthusiasts and wannabes. Thank you so very much Sir for your efforts and do keep up the great Training Series -- more examples and more projects pls Sir! Gracias!

TensorFlow what is with your face?

Cool video! Keep it going!

This is missing the main package Integrals.jl. I wouldn't recommend directly using the others for many reasons, including differentiability and boundary handling.

can mojo use Django?

Thats short and precise. Thanks!

Keep the good work, man! The community needs this.

Symbolics.jl is the symbolic system. SymbolicUtils.jl is just the underlying equation re-writing system. I would expect that most mathematical users use Symbolics.jl as SymbolicUtils.jl is really a developer's layer. Symbolics.jl is the layer that has all of the extra math features like derivatives, more advanced simplification, symbolic arrays, code generation, and more.

Really interesting content!

Great explanation What vsc extensions do u use?

Here is the link to the Github repository for the hardcore Mojicians: github.com/Numeryst/mojo-setup

I like that you illustrated with Windows and with Linux!

Ok, I think I had enough information for today.

mojo will replace all programming language.

This is really helpful, I have been searching for more helpful Julia content and found the perfect channel !

How did you create a directory? I am stuck in the Modular_Home error message when I try to run a mojo file on VS code. I been going at this for some time and I am getting stuck. I tried through Unbuntu, but I like VS code much better.

thank you.

What I understand in simple terms: A differentiable program is a program where the derivative can be systematically calculated with respect to each input parameter. The derivative (or vector of all derivatives, the gradient) represents the instantaneous rate of change, allowing us to determine in which direction to adjust the input parameters to achieve the desired effect on the output. By leveraging this information, the parameters can be adjusted step by step in the direction that most rapidly reduces a cost function. This approach helps identify optimal parameters, and this process is known as gradient descent. One of the most recognized uses of this idea is in machine learning. Machine learning models use these automatically calculated derivatives to reduce the difference between the model's predictions and the actual results. This continuous adjustment improves the model's performance over time, this process of refining parameters is what we call "learning."

will this extension work for long codes? like data analysis ?

High-quality video. Thank you!

I think you could have also included Meta.parse for this tutorial.

would you mind if you share the code_?

Maybe I am just too stupid to understand...

Very good content! You give us enough detailed and explained while still saving our time 🎉😊

That was so good and to the point!

Looks simpler than matplotlib.

A lot

Wowee that was dense, many things added to my list of things to check out :o

Thse are smart words coming from a smart man They sound like magic

how beautiful Julia syntax is, isn't it? maybe I am a little bias...

I was wondering if you can make a video of your background to know you or your motivations.Thank you for the real great material.