欢迎使用推荐系统！我们设计了本课程 以扩展您对推荐系统的了解，并解释 推荐中使用的不同模型， 和深度神经网络。
前提条件
本课程假定您满足以下条件：
- 已完成机器学习速成课程 参加线下或自学，或具备同等知识。
- 熟悉线性代数（内积、矩阵-矢量积）。
祝您学习愉快！
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2024-07-26。
[null,null,["最后更新时间 (UTC)：2024-07-26。"],[[["This course provides a comprehensive overview of recommendation systems and their various models, including matrix factorization and deep neural networks."],["Learners will gain an understanding of the key components of recommendation systems, such as candidate generation, scoring, and re-ranking, as well as the use of embeddings."],["The course requires prior knowledge of machine learning concepts and familiarity with linear algebra."],["Upon completion, learners should be able to describe the purpose of recommendation systems and develop a deeper understanding of common techniques used in candidate generation."],["The estimated time commitment for this course is approximately 4 hours."]]],[]]