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Santiago Profile picture
@svpino
Apr 12, 2021 • 8 tweets • 4 min read • Read on X
Scrolly
If you are looking to get a background in math before starting with machine learning, here is all the material you need covering the following topics:

• Probabilities & Statistics
• Linear Algebra
• Multivariate Calculus

More than enough to get started.

↓ 1/7
Seeing Theory

seeing-theory.brown.edu

An interactive website that will take you through some of the most critical concepts of Probabilities and Statistics.

These will be enough to get you started, and you will have fun while going through it!

↓ 2/7
Statistics 110: Probability

youtube.com/playlist?list=…

If you are looking for more, this course from Harvard University is an excellent introduction to probability as a language and a set of tools for understanding statistics, science, risk, and randomness.

↓ 3/7
Essence of Linear Algebra

youtube.com/playlist?list=…

Who doesn't love Grant Sanderson's YouTube videos?

Go through this playlist for a refresher in Linear Algebra, and you'll be more than ready to face any machine learning demons.

↓ 4/7
Linear algebra

ocw.mit.edu/courses/mathem…

MIT Course 18.06.

Taught by Profesor Gilbert Strang, this is simply one of the best linear algebra courses that you'll ever find.

Prof. Gilbert makes the subject ridiculously simple and engaging.

↓ 5/7
Mathematics for Machine Learning: Multivariate Calculus

coursera.org/learn/multivar…

A free, beginner-friendly introductory course to building your confidence and introduce you to the multivariate calculus required to build many common machine learning techniques.

↓ 6/7
Essence of Calculus

youtube.com/playlist?list=…

This is Grant Sanderson's excellent take on calculus.

A series of interactive videos that are informative and make calculus feel like something that you could have discovered yourself.

↓ 7/7
If you find these threads helpful, follow me @svpino for a weekly stream of machine learning content.

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More from @svpino

Santiago Profile picture
Nov 12
This is worth 1,000+ hours of engineering work every year:

1. Reproducing a bug
2. Getting detailed debug data
3. Writing how to reproduce it
4. Putting it all together in a good bug report

This tool can do all of this and cut the time it takes to fix the bug by 70%+:
makes the reporting and fixing process really fast!

Click once, and engineers get:

• Console logs
• Network requests
• Timing waterfall
• Repro steps
• Session & user details
• Device & OS
• Backend logs

Check the attached video. Jam.dev
It's just a browser extension - so anyone can report bugs w/ technical details.

Even after the bug just happened!

You can click instant replay, and Jam will create a detailed report with real-time data and video up to the last 2 minutes.
Read 4 tweets
Santiago Profile picture
Oct 1
My new soon-to-be Linux laptop right before I start assembling it. Image
RAM and SSD are now installed. Took me 1 minute and I didn’t even read the manual. Image
The packaging is very nice. A lot of cardboard. This thing comes well protected.

Mostly, frustration-free packaging. Reminiscent of Apple’s boxes. Image
Read 13 tweets
Santiago Profile picture
Sep 16
How can you build a good understanding of math for machine learning?

Here is a complete roadmap for you.

In essence, three fields make this up:

• Calculus
• Linear algebra
• Probability theory

Let's take a quick look at them! Image
This thread is courtesy of @TivadarDanka.

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…
1. Linear algebra.

In machine learning, data is represented by vectors. Essentially, training a learning algorithm is finding more descriptive representations of data through a series of transformations.

Linear algebra is the study of vector spaces and their transformations. Image
Read 9 tweets
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

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