Some of the most amazing applications of machine learning will probably come from folks working on brain-computer interface (BCI).

Check out this new paper discussing high-performance brain-to-text communication via handwriting.

Future of programming?

https://t.co/I5DUnE4vpy

Python Data Science Handbook.

An excellent book for those seeking an introduction to data science and machine learning.

Chapters have been converted to publicly available Colab notebooks.

Covers:
- NumPy
- Pandas
- Matplotlib
- ML algorithms

https://t.co/KjBpsDhDDJ

🔥 Applied Deep Learning - 2021 🔥

Just came across this absolute gem.

200+ free video lectures on all sorts of topics related to deep learning.

From Graph Attention Networks to RL algorithms.

repo: https://t.co/2IRF3CcR5p

playlist: https://t.co/N4zqkqnfdu

Some pretty amazing slides and notes for this Data Structures course.

https://t.co/JF7FSFKFT6

Recently, I shared a list of some courses that were useful in my transition to machine learning.

While I took most of the courses in person, there are some alternative online courses you can check out.

Here is a thread of a few interesting online courses based on the list:

⭐️ Linear Algebra ⭐️

A classical online course by Professor Gilbert Strang based on his popular textbook "Introduction to Linear Algebra". Learn about matrix theory and systems of

⭐️ Introduction to Complex Analysis ⭐️

Learn about the geometry of complex

⭐️ Differential Calculus ⭐️

Pay close attention to the chain rule as it's heavily referenced in machine learning, specifically when discussing optimization. This course is part of a specialization called MathTrackX. I recommend checking that as

⭐️ Information Theory ⭐️

When you are working with machine learning algorithms applied to data you are dealing with information processing which in essence relies on ideas from information theory such as entropy. This course should provide the

There is a lot of interest in large language models.

As a developer or researcher, you are probably looking for a guide on how to ramp up on LLMs.

Here is a study plan you could try:

My suggested study plan for now: 1) get familiar with concepts, 2) learn about Transformers, 3) learn about the foundation models, 4) continue keeping track of code, papers, and results, and 5) get building.

1) CS224N: Natural Language Processing with Deep Learning

It doesn't hurt to learn some of the concepts first. Depending on your level of knowledge it might be suitable to try a more basic

2) Stanford CS25 - Transformers United

Learn all about transformers in this fantastic

3) Foundation Models

You can learn about all types of foundation models like GPT-3, DINO, CLIP, Codex, among

Machine Learning Notes

I've been writing notes introducing some of the most important topics in AI today.

This thread lists a few notes I've published so far:

Introduction to Machine

Introduction to Deep

Introduction to Sequential

Introduction to Deep Computer