Creating and maintaining a data science environment
is no easy task.
>From installing libraries to updating runtimes,
it can be a real drain on both your time and productivity.
What if I told you there was a tool that could make all of that go away
so you can focus on the data science and machine learning?
What might such a tool look like?
Stay tuned to find out.
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Welcome to AI Adventures,
where we explore the art, science, and tools of machine learning.
My name is Yufeng Guo, and on this episode,
we're going to take a look at how Colab can help you learn data science
and experiment with your machine learning models.
Colaboratory, or Colab for short,
is a tool for machine learning education and research.
In a nutshell, it's basically a Jupyter Notebook environment
that requires no setup to use,
and I mean zero setup.
Since it runs in the browser, there's no local installation,
no updating of libraries, no managing of Python environments.
Colab comes with lots of Python libraries preinstalled
so
It also saves directly to Google Drive
so you can keep your work up to date
without needing to worry about it getting lost.
This means you get sharing and comments just like in Google Docs.
This totally changes the collaboration options
when it comes to data science.
Speaking of collaboration, Colab also has revision history
just like the rest of your Google Drive files.
So you can open an old version, download it,
or just revert to that version entirely if you want.
Of course, I've been leaving out one of the top features of Colab
that users absolutely love.
You can use a free GPU in Colab.
No credit card, no signups.
Just go on over and change at runtime to include a GPU accelerator.
While you're there, you might also notice
that you can attach a TPU instead of a GPU.
So a TPU, or Tensor Processing Unit, is Google's accelerator,
custom-built for machine learning.
And it enables some super fast training of advanced machine learning models.
I'll post some resources down in the description
for those who are interested in learning more.
On the left-hand side of your notebook, there's an often missed menu
which gives you access to three areas:
the
Let's take a look at the Table of Contents first.
If you don't already keep your notebook cells nicely annotated,
now there's a nice incentive to do so.
The Table of Contents displays these handy headers
that are clickable to help you navigate through your notebook
and keep track of its overall structure.
Next up in the menu are the code snippets.
These are an absolute goldmine of useful samples that you can use
from uploading and downloading files,
to visualizations, and even capturing images with the camera.
Best of all, each snippet links out to a full Colab notebook
where you can actually try the code in its own environment
before copying it into your own.
Finally, a handy-dandy file browser
is there to help you keep track of your files
so you don't need to keep running bang LS (
to see what is saved on that machine.
Learning data science and machine learning is hard enough as is.
So skip the hassle of installing, setting up, and maintaining
your own environment by using Colab.
Thanks for watching this episode of
and if you enjoyed it, please be sure to like and subscribe
to get all the latest episodes right when they come out.
For now, head over to Colab to get your code running right now.
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