All of my notes on programming, cheat sheets, and more, collected in chronological order.
The M3 chip uses ARM architecture. As of early 2024, TensorFlow has **native support** for Apple Silicon (M1/M2/M3 chips). This means you can run TensorFlow efficiently on your M3 Mac without needing to use Rosetta 2 for translation
Conda is a data science package and environment management application. It primarily manages Python packages but also supports R, Ruby, Lua, Scala, Java, JavaScript, C, C++, and Fortran packages
As a full stack engineer with knowledge of Python and Ruby on Rails, here are some basic packages and tools I have (updated for Silicon macOS)
These are my Must / Nice to have on a MacbookPro over the years - (updated for Apple Silicon Macs)
I'currently taking a AI/ML course so my choice for working with python is Conda. However, I'm used to pyenv so I wanted to try if my previous setup notes on a Intel MacBook were still valid.
The M3 chip uses ARM architecture, so I prioritize software and tools that have native ARM support for optimal performance. Here are the must-have steps I inherited from my Intel configuration.
Just as I started my ML/AI bootcamp, my old MacBook Pro's audio stopped working. Repairs would have taken weeks, so I took a leap and invested in an M3 MacBook. It's a game-changer! and setting it up for development has been quite an adventure. I've been taking notes during the setup process, just in case I need to repeat these steps in the future. Here are the first steps.
Welcome to my Git workflow! 🌟 I've followed this approach to keep my development process smooth, organized, and enjoyable. (Migrated from github's gists original)