https://xiongneng.me/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/飞污熊小站Hugo 0.165.0 & FixIt v0.4.6-20260512073637-464c4659zh-CNyidao620@163.com (XiongNeng)yidao620@163.com (XiongNeng)XiongNengSun, 23 Aug 2026 07:23:39 +0000https://xiongneng.me/posts/ai/ml-libs/Fri, 02 Jan 2026 16:11:33 +0800yidao620@163.com (XiongNeng)https://xiongneng.me/posts/ai/ml-libs/AI<h3 class="heading-element" id="基础科学计算库"><span>基础科学计算库</span> <a href="#%e5%9f%ba%e7%a1%80%e7%a7%91%e5%ad%a6%e8%ae%a1%e7%ae%97%e5%ba%93" class="heading-mark"> <svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg> </a> </h3><ol> <li>‌NumPy‌提供多维数组对象及高效数学运算功能，是TensorFlow等库的底层依赖。</li> <li>‌Pandas‌用于数据清洗与结构化操作，支持DataFrame和Series数据结构，适合表格数据处理。</li> <li>SciPy‌扩展NumPy功能，提供科学计算算法（如优化、信号处理）</li> </ol> <h3 class="heading-element" id="传统机器学习库"><span>传统机器学习库</span> <a href="#%e4%bc%a0%e7%bb%9f%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e5%ba%93" class="heading-mark"> <svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg> </a> </h3><ol> <li>‌Scikit-learn‌包含分类、回归、聚类等经典算法，API设计统一，适合快速实现标准模型。</li> <li>‌XGBoost/LightGBM‌梯度提升框架，擅长处理结构化数据，在Kaggle竞赛中广泛应用。</li> <li>‌StatsModels‌侧重统计建模，支持假设检验和回归分析。</li> </ol> <h3 class="heading-element" id="深度学习框架"><span>深度学习框架</span> <a href="#%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e6%a1%86%e6%9e%b6" class="heading-mark"> <svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg> </a> </h3><ol> <li>‌TensorFlow‌Google开发的工业级框架，支持分布式训练，适合大规模模型部署。</li> <li>‌PyTorch‌Facebook主导的动态图框架，研究场景首选，调试灵活。</li> <li>‌Keras‌高层API接口，可基于TensorFlow或PyTorch后端快速构建神经网络。</li> </ol> <h3 class="heading-element" id="其他工具库"><span>其他工具库</span> <a href="#%e5%85%b6%e4%bb%96%e5%b7%a5%e5%85%b7%e5%ba%93" class="heading-mark"> <svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg> </a> </h3><ul> <li>‌Matplotlib/Seaborn‌：数据可视化</li> <li>‌OpenCV‌：计算机视觉任务</li> <li>‌NLTK/Gensim‌：自然语言处理</li> </ul> <p>这些库覆盖了从数据预处理到模型部署的全流程，其中Scikit-learn、TensorFlow和PyTorch为当前主流选择</p>