Thread by @svpino

It was a great improvement when I learned to use notebooks!

▫️To run experiments
▫️To share my code
▫️To present my work

It's a very different dynamic!

If you are a Python 🐍 developer, notebooks will be a multiplier for your career.

Let's talk about them:

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A notebook is an "interactive computing environment." 🤓

This means that you can:

▫️Write code
▫️Use widgets
▫️Plot charts
▫️Write text (Markdown!)
▫️Write equations
▫️Display images
▫️Display videos

All of this in the same place! Like an interactive book!

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Notebooks contain "cells":

▫️You can write code on each cell (or anything, really)
▫️You can execute each cell independently
▫️Memory is shared across cells

These last two points are huge and one of the main draws of notebooks for new developers!

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Do you need to load some data and it takes a while?

▫️You write the code in a cell
▫️You run it once
▫️You don't need to ever run the cell again

Every cell acts as an independent "program" that shares the memory with every other program.

This makes notebooks very useful!

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But, wait a minute... How are notebooks going to help you?

Notebooks are good for experimenting and presenting results. They aren't meant to write production code!

Do you want to rapidly prototype a function? Maybe compare two options? Notebooks are great for that!

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They also have drawbacks:

▫️They discourage reusability
▫️They encourage global access to data
▫️Source control is not great
▫️The editor is not as powerful as an IDE

You would never open a can of tuna with a drill, right?

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Jupyter is the defacto standard for notebooks.

Jupyter is open-source, runs everywhere, and it's used across the board.

Here is the documentation of The Jupyter Notebook: jupyter-notebook.readthedocs.io/en/stable/note…

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Personally, I love Google Colab:

▫️It's integrated with my Google account
▫️It's free
▫️I can easily share them
▫️It gives me access to free GPU/TPU resources!

I can't even begin to express the importance of that last point! If you are into Machine Learning, you understand.

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A small tiny step you can take today:

▫️Introduction to Python
▫️Introduction to Google Colab

It will take around 10 - 15 minutes.

This is a great springboard that will help you understand notebooks and get into Machine Learning later.

colab.research.google.com/github/tensorf…

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Ready for a 30-minute introduction to Jupyter Notebooks?

"Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough" from @CoreyMSchafer is gonna give you all you need.

Video:

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Did I mention you can share Colab notebooks?

Here is a link to a notebook I built to multiply two numbers with a neural network.

colab.research.google.com/github/svpino/…

The code it's not that interesting. But feel free to follow the link and run each cell to see how it works.

Good luck!