欢迎使用GitHub 安装 sudo apt-get install python-setuptools python-numpy python-scipy python-matplotlib python-pip -y sudo pip install numpy scipy matplotlib scikit-learn luminol 设置日志 您必须提供日志文件的位置才能运行此程序。 以下是任何Web服务器的日志格式 “％d-％b-％Y％T ::::％a ::::％m ::::％s ::::％B ::::％D ::::％U ::: ：％r“ ％d是日期 ％b是月份 ％Y是年份 ％

scikit-learn : Machine Learning Simplified: Implement scikit-learn into every step of the data science pipeline By 作者: Raul Garreta – Guillermo Moncecchi – Trent Hauck – Gavin Hackeling ISBN-10 书号: 1788833473 ISBN-13 书号: 9781788833479 Release 出版日期: 2017-11-10 pages 页数: (530 )

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron English | 13 Mar. 2017 | ASIN: B06XNKV5TS | 581 Pages | AZW3 | 21.66 MB Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details

叶识别 一个用于从叶子图像识别物种的python桌面应用程序。 使用图像处理和机器学习的概念。 它分为以下7种 槭树 雪松杜达拉 紫荆 柑桔 银杏叶 鹅掌 夹竹桃夹竹桃 要运行项目，请运行Executioner.py 有关更多详细信息，请参阅Project Details.pdf

Python scikit_learn-1.0.2-cp38-cp38-win_amd64.whl

资源来自pypi官网，解压后可用。 资源全名：scikit-learn-0.18.2.win32-py3.5.exe

Scikit学习教程 一组用于scikit学习自学习的示例。 工作正在进行中... 本教程正在创建中。 还没结束 如何衡量模型性能 标准指标精度，召回率，F1指标- 该示例显示了如何计算基本分类器度量值，例如精度，召回率，f1 文件： 精确召回曲线 示例说明了如何在理想的随机情况下解释精确调用曲线。 如果两个模型的曲线看起来相似该怎么办。 文件： 开发环境 python> 3.6 吹牛 sklearn> 0.21.3

torch-1.0.1-cp37-cp37m-win_amd64.whl tensorflow-1.15.3-cp37-cp37m-win_amd64.whl --cpu版本 scipy-1.5.2-cp37-cp37m-win_amd64.whl scikit_learn-0.23.1-cp37-cp37m-win_amd64.whl python-3.7.6-amd64.exe pyHook-1.5.1-cp37-cp37m-win_amd64.whl numpy-1.19.0+mkl-cp37-cp37m-win_amd64.whl matplotlib-2.2.5-cp37-cp37m-win_amd64.whl Keras-2.3.1-py2.py3-none-any.whl pywin32-221.win-amd64-py3.7.exe

不错的scikit-learn（中文文档） 不错的scikit-learn（中文文档）

【高清】这个版本只包含第一部分的前九章的内容，相比于第一版，增加了无监督学习等内容。