trekhleb/homemade-machine-learning Alternatives

calls homemade-machine-learning “Choose for interactive Jupyter demos with math explanations and a friendly from-scratch approach.”

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.

calls homemade-machine-learning “choose for English, math-heavy from-scratch implementations of the same algorithms.”

100-Days-Of-ML-Code中文版

calls homemade-machine-learning “Use this if you want broader ML algorithms with explanations in Jupyter rather than PRML-specific content”

PRML algorithms implemented in Python

calls homemade-machine-learning “Choose when you want a hands-on, actively maintained Python course with from-scratch algorithm demos and math explanations.”

:speech_balloon: Machine Learning Course with Python:

calls homemade-machine-learning “Choose for interactive, math-explained Python implementations of classic ML algorithms; more hands-on and less exam-oriented.”

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

calls homemade-machine-learning “Choose this for detailed math-driven Jupyter implementations of classical ML algorithms instead of a broad Chinese survey.”

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

calls homemade-machine-learning “Choose this when you want Python interactive demos and math explanations instead of static notes.”

吴恩达老师的机器学习课程个人笔记

calls homemade-machine-learning “choose when you want interactive Jupyter demos with math explanations and visualizations.”

Minimal and clean examples of machine learning algorithms implementations

calls homemade-machine-learning “When you want to understand ML algorithms by implementing them with NumPy and interactive demos, not just using high-level libraries.”

A collection of machine learning examples and tutorials.

calls homemade-machine-learning “Pick for learning algorithms by implementing from scratch in Python rather than applying scikit-learn.”

Notebooks and code for the book "Introduction to Machine Learning with Python"

calls homemade-machine-learning “Choose for from-scratch implementations of classic ML algorithms, narrower than deep learning.”

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

calls homemade-machine-learning “Covers classical ML algorithms only, a subset of the seed's scope.”

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。