微软 (英文名称:Microsoft;中文名称:微软公司或美国微软公司)始建于1975年,是一家美国跨国科技公司,也是世界PC(Personal Computer,个人计算机)软件开发的先导,由比尔·盖茨与保罗·艾伦创办于1975年,公司总部设立在华盛顿州的雷德蒙德(Redmond,邻近西雅图)。以研发、制造、授权和提供广泛的电脑软件服务业务为主。

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微软机器学习课程(Machine Learning for Beginners, Curriculum)来了,一天之内狂揽 2000 + 星。

课程地址:https://github.com/microsoft/ML-For-Beginners

该课程面向机器学习初学者,总共 12 周、24 节课,完全免费,已经过 MIT 授权。由 Azure 云倡导者等人员制作而成。

这门课程都是关于「经典机器学习」的,使用 Scikit-learn 库来处理 ML 基本概念。不过本次 ML 课程中不讨论深度学习或神经网络相关内容。

Scikit-learn 库:https://scikit-learn.org/stable/user_guide.html

这门课程涉及到的算法都有具体的示例,包括回归(北美南瓜市场定价示例)、分类(泛亚洲菜系示例)、聚类(尼日利亚音乐品味示例)、NLP(欧洲酒店评论示例)、时间序列(世界用电量示例),强化学习(俄罗斯关于彼得和狼的故事)。

这是一门自学课程,但它在以小组为单位的学习中效果很好,因此你可以考虑寻找学习伙伴并一起学习。通过课前测验热身,和小伙伴一起或单独完成课程和作业。通过课后测验测试自己掌握的知识。学习这门课之前,最好掌握 Python。

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最新论文

Multi-label image recognition is a fundamental yet practical task because real-world images inherently possess multiple semantic labels. However, it is difficult to collect large-scale multi-label annotations due to the complexity of both the input images and output label spaces. To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i.e., merely some labels are known while other labels are missing (also called unknown labels) per image. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations to transfer knowledge of known labels to generate pseudo labels for unknown labels. Specifically, an intra-image semantic transfer module learns image-specific label co-occurrence matrix and maps the known labels to complement unknown labels based on this matrix. Meanwhile, a cross-image transfer module learns category-specific feature similarities and helps complement unknown labels with high similarities. Finally, both known and generated labels are used to train the multi-label recognition models. Extensive experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.

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