Machine-Learning-Guide

Why we included this project

For anyone trying to get a handle on the machine learning landscape before choosing a stack, this guide is a solid starting point. It collects learning resources and then organizes the major frameworks, libraries, and tools by what they do, covering LLM training, local deployment, and development in PyTorch, TensorFlow, and Core ML. The sections on running large language models locally and the algorithm overview are particularly useful for a quick survey of what's out there. Since it's a curated index rather than a single installable product, think of it as a map for research and comparison, not something you deploy directly.