Here, we list out a few resourses that you may find helpful when climbing the steep Ivy learning curve.
Docs for respective Backends
Tensorflow Documentation, PyTorch Documentation, NumPy Documentation and Jax Documentation are the most useful resources to find your way through the behaviours from different backends. These are the most important resources when working on Docstrings, Ivy Frontends and Ivy Frontends tests.
Python - Reference
realpython and pynative are very useful for any kinds of help regarding Python.
Stack Exchange/ Stack Overflow
A good platform to search for any sort of information regarding python and ML. Useful when working on almost any section in the Deep Dive.
GitHub Co-Pilot can be used to write any bit of code in Ivy. They are often very useful when developing code and also helps gets things done faster.
GitHub - Reference
Git docs is the first place you must head to when you stuck which any issue related to git.
IDE extension for spell checking
Though this may sound odd, a spell-checking extension is very useful to people contributing to Ivy when adding docstrings.
Docker Documentation is the best place to learn more about docker.
GitHub Actions can be best place to understand Continuous Integration and how testing is done to keep our repo error free.