Ask HN: In 2023 which is the best path to get good at machine and deep learning?
Which is the best resource (book, public course, blogs, etc) to get started in machine and deep learning and then get good at it both as a practitioner and from theoretical understanding?
The ultimate goal is to become a good at implementing models and come up with new ones.
Is there something like teachyourselfCS but for Data Science, ML and DL?
12 comments
[ 0.21 ms ] story [ 32.6 ms ] threadkarpathy's Zero to Hero series (https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThs...)
meta llama 2 - is open source https://github.com/facebookresearch/llama/tree/main
Tools:
ai - hosting Banana - Machine Learning Model Deployment on Serverless GPUs https://www.banana.dev
pinecone - vector database: https://www.pinecone.io
how to run AI language models on a single cpu pc - https://news.ycombinator.com/item?id=34869960
and a lot of open-source Notebooks at github.com/pinecone-io/examples which demonstrate techniques from semantic search to question and answering to retrieval augmented generation (RAG)
FWIW, our free tier is sufficient to use any of these notebooks or our open source vercel templates - so whether you're a student or just a solo dev looking to learn, you can create a free account and get started and never pay a dime - the one limitation is you can only have one index live at a time - so delete it once you're finished experimenting with a given notebook.
Starting is the best way to get started.
Stasis cannot be motion optimized. Motivation is the hardest part. Everything else is about equally difficult because all the rest is experience. Good luckz.
For some people, having some suggested learning tracks can help them have more confidence that they are moving in the right direction and not just chasing their tails.
The possibility can’t be optimized away except by not starting.
If you aren’t doing something badly and inefficiently, you aren’t learning.
The first problem isn’t finding the best path (per the question). The first problem is to stop standing still.
Searching for the best path is only pretending to learn.
I’m simply saying if you come across someone who is lost and without a map, it is helpful to at least give some basic direction.
I think compasses have more utility than opinions drawn from different terrain.
The Watch the Caltech telecourse - https://work.caltech.edu/telecourse
Read tutorials on Pytorch, Tensorflow & Keras.
Read, source codes on hugging face and deploys, test, train toy models.
Test your skills by participating in Data scientist competitions like Kaggle or Numerai.
It will give you a great way of guaging your competence with other data scientists.
the heavy way: kevin murphy's a probabilistic approach to machine learning. you could make use of this book basically every day.