>> The segment of the AI Show, Cool Libraries.
[MUSIC].
>> All right, Suraj, I thought you would introduce yourself.
Tell us who you are, what you do, my friend.
>> Sure. My name is Suraj Subramanian.
I am working at PyTorch as a Developer Advocate.
That means I speak to other developers who
use PyTorch for their deep learning journey,
and I like to further understand what they're using PyTorch for,
and see how I can make that a little easier.
>> Fantastic. Let's start first with what is PyTorch?
Give us a brief overview of what
PyTorch is for those that don't know.
>> Yeah, PyTorch is
an open-source library that you can use to build and run
deep learning models similar to what you have
been using for [inaudible].
If you're familiar with NumPy,
PyTorch brings all of that scientific computing goodness on
new hardware accelerators like GPUs and TPUs and all of that.
>> That's awesome. Why is PyTorch a good
library to get started with when it comes to deep learning?
>> The primary reason is the API,
PyTorch API is really simple,
but it's also very powerful. It's pythonic.
If you're familiar with programming in PyTorch at all,
you'll find yourself at home using PyTorch.
Another really good reason to start with
PyTorch is because it's easy to debug.
The dynamic nature of PyTorch really
allows you to debug your code very easily.
You can just plug in print statements just
to keep track of what's happening in the code.
It also helps you understand how neural networks work.
It really helps me get a better intuition
of what's happening under the hood inside of neural network.
>> That's awesome. Where can people go?
If someone's like, "I'm totally excited.
I have the website just right over here".
Where should people go to get started?
By the way, you just had a 1.8 release. Is that correct?
>> Yes. We just released that yesterday.
>> See, timely.
Let's talk about if people want to get started,
what's the best way to learn the basics?
>> To learn the basics,
you head to the Tutorial step on the website.
>> Let's go there. Okay.
>> This section exactly titled Learn the Basics.
>> What? This is cool. So tell us about this.
>> So this is the 101 tutorial to PyTorch that Seth,
Cassie, Dmitri, Ari, and myself collaborated to build.
It's a great way to get started with PyTorch
because it's a very step-by-step guide
to every single station in a typical ML workflow.
It starts with the very basics answers,
which are the building blocks of basically everything in PyTorch.
It progresses sequentially into how to get your data up,
prepare and load it into your model.
How do you actually build a neural network?
What's actually happening within the neural network when you click
or when you run backward which sets the training loop on.
Also, how do you persist the neural network
so that you can load it for later use?
>> I mean, this is really cool.
I mean, I'm looking at this and I
might self-servingly saying this is really cool, right?
Because like we helped to write it.
But the thing that I like about this is it
comes from the developers' perspective.
Look, it's really cool for me to look at this because like I said,
it's fun and you can probably get through this.
How long do you think it'll take for someone to get through this?
>> Probably, I wouldn't say it takes anything more than an hour.
>> Got it.
>> Even if you run all of the code in this,
so it's a really quick way to get started.
>> So who is this for?
For people who are watching,
who would do really well with this?
>> I'd say, it's for anyone
who wants to get familiar with PyTorch.
All you really need is some familiarity with
PyTorch programming and a basic intuition
of what happens in a neural network.
Once you have that,
it's really easy to relate the concepts in neural network theory
or neural network training to
the actual code if you follow along this guide.
So the way the guide is structured
is that there is an explanation and then there's
a relevant code snippet that you can
actually run in a Google Colab notebook as well.
So you can instantly relate what you're
reading to the actual code and see what the output is.
I feel like that just solidifies
your understanding of what happens in the neural network.
>> Absolutely. Like I said, for me,
the quickstart that I liked the most is it literally is
end-to-end if you know already what's going on,
literally here's the code.
>> Yeah, this page is specifically for
people who are more familiar with deep learning.
For instance, maybe they have
used deep learning in other frameworks and
they just want to have a quick skim
through what PyTorch does and how does it do it?
What are the relevant sub-modules
in PyTorch that do particular tasks?
This is a Text light,
Code heavy sort of page where you get a whole snapshot of
what's happening throughout the ML workflow
and PyTorch in just a single web page.
>> The cool thing is if you look over here,
let me do a little zoom in here.
I need to move my mouse here. There you go.
If you do a little zoom in here,
you're going to see like every section that one would need,
like you need to start with data.
You need to make a model. You need to construct the model shape.
Then you optimize the model parameters,
and then you got to save it and then you've got to use it.
That's literally, I think at least in my opinion,
this is all it needs to be done.
This is like the whole process in a single nutshell,
and you see how to do it with PyTorch every step of the way.
Then you can dive in to
understand each one of these concepts a little bit more.
That's my favorite part of this. All right.
So thank you so much, Suraj.
This has been awesome.
For those that don't know, just go to
PyTorch.org and learn the basics in the tutorial.
Thanks for watching and we'll see you next time.
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