ML resources

Last updated: March, 2024

Some resources that i’ve used, they are a little bit biased to LLMs because is what i like the most, but they are still useful for any ml enthusiast.

Concepts, websites, videos, books, papers and more.

I’ll keep updating this list as i find more.

how to start

• Linear regression, logistic regression, gradient descent, backpropagation, neural networks, mlps, cnn, rnn, lstm, encoder-decoder architecture, transformers.

maths

• 3blue1brown’s linear algebra season.
• 3blue1brown’s calculus season.
• ”maths for machine learning” — i didn’t read it all, but i used it as a reference when i needed to understand or remember a concept.

ml theory

this resources are focused on how ml works under the hood.

• 3blue1brow’s neural networks season.
• ”the little book of deep learning”.

some ‘advanced’ stuff

• andrej karpathy’s youtube channel — simply the goat.
• papers that i’ve read — some papers that i’ve saved, probably stoled from someone else list and added to mine. they are not in any particular order, and i’m pretty sure that i’ve missed some of them because i usually don’t track what i read.

websites

• hugging face
• arxiv

tips

• build stuff.
• build stuff.
• build stuff.
• build stuff.

Almost any basic, and code related, solution can be found in the internet. Just search for it and build stuff.