@GitHub_Daily
7/9/2025, 4:00:00 AM
GitHub 上一份超全面的机器学习实战手册：machine-learning，覆盖了机器学习的完整技术栈。 既有从零手写算法帮我们理解底层原理，又结合 scikit-learn、PyTorch 等主流库的实际应用，并通过 Jupyter Notebook 呈现所有内容。 GitHub：http://github.com/ethen8181/machine-learning 主要内容： - 深度学习：从 Softmax 回归到 Transformer、BERT，包含 CNN、RNN、GNN 等完整技术栈 - 推荐系统：矩阵分解、协同过滤、内容推荐等核心算法实现 - 树模型：决策树、随机森林、XGBoost、LightGBM 从原理到调优 - 模型部署：FastAPI、Kubernetes、AWS 等生产环境实战指南 - 时间序列：指数平滑、监督学习建模等预测方法 - Python 编程：并行计算、Cython 优化、设计模式等进阶技巧 学习者需要有一定的 Python 基础，适合想要深入理解机器学习技术栈的开发者。
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