AiLearning-Theory-Applying

Why we included this project

This is a Chinese-language learning path, not a deployable tool, so treat it as a guided course rather than a library to drop into a project. It starts with the math you actually need, from calculus and linear algebra to probability and activation functions, then moves through machine learning, deep learning, and Transformer-based NLP such as BERT. The notebooks are heavily commented and ship with datasets, so you can run each example yourself and check the result. That makes it a good fit for developers and students who want one ordered sequence instead of stitching together scattered tutorials, and the Kaggle-style competition sections show how the theory turns into working solutions on real benchmark problems. If you are self-teaching or bringing a junior teammate up to speed, this is a solid resource to work through.