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

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