tangyudi/Ai-Learn Alternatives

Fresh, comprehensive AI engineering learning path in English; choose for a modern hands-on curriculum instead of the Chinese seed.

Learn it. Build it. Ship it for others.

Comprehensive deep-learning/CV notes from popular courses; choose for a current, note-based deep learning path rather than the seed's case-based route.

✅（已完结）超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】

Older but still useful ML-only notebook collection; choose when you need a compact ML tutorial set, not a full AI roadmap.

A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

Aged but broad data-science notebook collection; choose if you want lots of pre-written notebook examples over a structured roadmap.

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

A TensorFlow 2.0 port of the Deep Learning book; choose it if you want a focused DL book in TensorFlow, not a multi-topic roadmap.

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的认可

Structured open ML course with assignments; choose it for an in-depth ML-only course instead of a multi-domain AI roadmap.

Open Machine Learning Course

A data-science roadmap, narrower than the seed's full AI scope; choose if you only need a data science path.

Data Science Roadmap from A to Z

Older practice notebooks for ML techniques; choose for standalone ML practice, not a full AI curriculum.

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

A set of deep learning examples (100 cases) in TensorFlow/PyTorch, narrower than the seed's full AI roadmap.

深度学习100例、深度学习DL、图片分类、目标识别、目标检测、自然语言处理nlp、文本分类、TensorFlow、PyTorch

A classic data science handbook; choose for a deep dive into pandas/scikit-learn, not for DL/NLP.

Python Data Science Handbook: full text in Jupyter Notebooks

Older but clear from-scratch implementations of ML algorithms; choose to learn algorithm internals rather than a broad roadmap.

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.