Quote Inspired by the original Reddit post. Let me put you on some good stuff.
Last updated: Dec 2023
For learning ML
- This book
- Linear Algebra, Calculus, Statistics, Probability - (Uni, High School) OR (Read textbooks, watch Khan Academcy)
- Hastie
- Tibrishani
- AndrewNg
- Statquest
- Intuitive Machine Learning
- 3B1B
- HackerNews
- https://www.coursera.org/specializations/machine-learning-introduction
- Datacamp
- Tutorials
- Read the Docs: https://scikit-learn.org/stable/ and https://docs.scipy.org/doc/scipy/reference/optimize.html#
For deep learning
- Andrej Karthov
- Statquest
- 3B1B
- Put tensorflow in a Linux Box
- Arvix lite
- The deep learning book
- Now forget all of that and read the deep learning book. Put tensorflow and pytorch on a Linux box and run examples until you get it. Do stuff with CNNs and RNNs and just feed forward NNs.
- Hugging face
- https://karpathy.ai/zero-to-hero.html
- https://www.coursera.org/specializations/deep-learning
For practice
- This book - listen to some classical music and read it
- Other Internships
- Even just interviews (for practice)
- Leetcodes
- HackerRank
- Kaggles
- Neetcode
- R
- Projects
For staying up to date
- HackerNews
- Arvix Lite
- Long form educational videos to fall asleep to here
For Coding
- https://theartofhpc.com
- Leetcode
- Neetcode https://neetcode.io/roadmap, all of it, in both Python and C++
- LeetCode 75 (there will be overlap)
- SQL 50
- Pandas 10
- Hackerrank
- Do their
- Projects
Neetcode Roadmap
DSX Checklist
And remember, above all:
10,000 hours of deliberate practice will make you an expert. You can iterate as you work. Only compare yourself to the past, not to others. - Andrej Karpathy
https://youtu.be/VMj-3S1tku0?si=l3kXAhaTof-eHM06 5:00
https://youtu.be/aircAruvnKk?si=OJXBb_P056pVYJCw
A repository of good repositories