تنظيم صفحاتك في مجموعات
يمكنك حفظ المحتوى وتصنيفه حسب إعداداتك المفضّلة.
مرحبًا بك في أنظمة الاقتراح. لقد صممنا هذه الدورة
لتوسيع نطاق معرفتك بأنظمة التوصية وتوضيح
النماذج المختلفة المستخدمة في التوصية، بما في ذلك المصفوفة
التحليل إلى العوامل والشبكات العصبية العميقة.
تاريخ التعديل الأخير: 2024-07-26 (حسب التوقيت العالمي المتفَّق عليه)
[null,null,["تاريخ التعديل الأخير: 2024-07-26 (حسب التوقيت العالمي المتفَّق عليه)"],[[["\u003cp\u003eThis course provides a comprehensive overview of recommendation systems and their various models, including matrix factorization and deep neural networks.\u003c/p\u003e\n"],["\u003cp\u003eLearners 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.\u003c/p\u003e\n"],["\u003cp\u003eThe course requires prior knowledge of machine learning concepts and familiarity with linear algebra.\u003c/p\u003e\n"],["\u003cp\u003eUpon completion, learners should be able to describe the purpose of recommendation systems and develop a deeper understanding of common techniques used in candidate generation.\u003c/p\u003e\n"],["\u003cp\u003eThe estimated time commitment for this course is approximately 4 hours.\u003c/p\u003e\n"]]],[],null,["# Introduction\n\n\u003cbr /\u003e\n\n| **Estimated course time:** 4 hours\n\nWelcome to **Recommendation Systems**! We've designed this course\nto expand your knowledge of recommendation systems and explain\ndifferent models used in recommendation, including matrix\nfactorization and deep neural networks.\n| **Objectives:**\n|\n| - Describe the purpose of recommendation systems.\n| - Understand the components of a recommendation system including candidate generation, scoring, and re-ranking.\n| - Use embeddings to represent items and queries.\n| - Develop a deeper technical understanding of common techniques used in candidate generation.\n\nPrerequisites\n-------------\n\nThis course assumes you have:\n\n- Completed [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/) either in-person or self-study, or you have equivalent knowledge.\n- Familiarity with linear algebra (inner product, matrix-vector product).\n\n*Happy Learning!*"]]
