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Santiago Profile picture
@svpino
Oct 1, 2020 • 11 tweets • 5 min read • Read on X
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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:

🧵👇
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!

👇
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!

👇
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!

👇
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!

👇
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?

👇
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…

👇
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.

👇
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…

👇
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:

👇
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!

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More from @svpino

Santiago Profile picture
Aug 12
The single most undervalued fact of linear algebra:

Matrices are graphs, and graphs are matrices.

Encoding matrices as graphs is a cheat code, making complex behavior simple to study.

Let me show you how! Image
By the way, this thread is courtesy of @TivadarDanka. He allowed me to republish it.

3 years ago, he started writing a book about the mathematics of Machine Learning.

It's the best book you'll ever read:



Nobody explains complex ideas like he does.tivadardanka.com/books/mathemat…
If you look at this example, you probably figured out the rule.

Each row is a node, and each element represents a directed and weighted edge. We omit any edges of zero elements.

The element in the 𝑖-th row and 𝑗-th column corresponds to an edge going from 𝑖 to 𝑗. Image
Read 18 tweets
Santiago Profile picture
Jul 12
A common fallacy:

If it's raining, the sidewalk is wet. But if the sidewalk is wet, is it raining?

Reversing the implication is called "affirming the consequent." We usually fall for this.

But surprisingly, it's not entirely wrong!

Let's explain it using Bayes Theorem:

1/10 Image
This explanation is courtesy of @TivadarDanka. He allowed me to republish it.

He is writing a book about the mathematics of Machine Learning. It's the best book I've read:



Nobody explains complex ideas like he does.

2/10tivadardanka.com/books/mathemat…
We call propositions of the form "if A, then B" implications.

We write them as "A → B," and they form the bulk of our scientific knowledge.

For example:

"If X is a closed system, then the entropy of X cannot decrease" is the second law of thermodynamics.

3/10
Read 10 tweets
Santiago Profile picture
Jun 12
Some of the skills you need to start building AI applications:

• Python and SQL
• Transformer and diffusion models
• LLMs and fine-tuning
• Retrieval Augmented Generation
• Vector databases

Here is one of the most comprehensive programs that you'll find online:
"Generative AI for Software Developers" is a 4-month online course.

It's a 5 to 10-hour weekly commitment, but you can dedicate as much time as you want to finish early.

Here is the link to the program:

I also have a PDF with the syllabus:bit.ly/4aNOJdy


I'm a huge fan of online education, but most of it is all over the place and mostly theoretical.

This program is different:

You'll work on 4 different hands-on projects. You'll learn practical skills you can use at the office right away.cdn.sanity.io/files/tlr8oxjg…
Read 6 tweets
Santiago Profile picture
Jun 10
There's a stunning, simple explanation behind matrix multiplication.

This is the first time this clicked on my brain, and it will be the best thing you read all week.

Here is a breakdown of the most crucial idea behind modern machine learning:

1/15 Image
This explanation is courtesy of @TivadarDanka. He allowed me to republish it

3 years ago, he started writing a book about the mathematics of Machine Learning.

It's the best book you'll ever read:



Nobody explains complex ideas like he does.

2/15tivadardanka.com/books/mathemat…
Let's start with the raw definition of the product of A and B.

This looks horrible and complicated.

Let's unwrap it step by step.

3/15 Image
Read 15 tweets
Santiago Profile picture
May 28
This assistant has 169 lines of code:

• Gemini Flash
• OpenAI Whisper
• OpenAI TTS API
• OpenCV

GPT-4o is slower than Flash, more expensive, chatty, and very stubborn (it doesn't like to stick to my prompts).

Next week, I'll post a step-by-step video on how to build this.
The first request takes longer (warming up), but things work faster from that point.

Few opportunities to improve this:

1. Stream answers from the model (instead of waiting for the full answer.)

2. Add the ability to interrupt the assistant.

3. Whisper running on GPU
Unfortunately, no local modal supports text+images (as far as I know,) so I'm stuck running online models.

The TTS API (synthesizing text to audio) can also be replaced by a local version. I tried, but the available voices suck (too robotic), so I kept OpenAI's.
Read 4 tweets
Santiago Profile picture
May 25
I’m so sorry about anyone who bought the rabbit r1.

It’s not just that the product is non-functional (as we learned from all the reviews), the real problem is that the whole thing seems to be a lie.

None of what they pitched exists or functions the way they said. Image
They sold the world on a Large Action Model (LAM), an intelligent AI model that would understand applications and execute the actions requested by the user.

In reality, they are using Playwright, a web automation tool.

No AI. Just dumb, click-around, hard-coded scripts. Image
Their foundational AI model is just ChatGPT + scripts.

Rabbit’s founder lied on their marketing videos, during interviews, when he presented the product, and lied on Discord when answering questions from early supporters.

And that’s just the beginning:
Read 4 tweets

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