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Simpan dan kategorikan konten berdasarkan preferensi Anda.
Selamat datang di Sistem Rekomendasi! Kami telah merancang
materi ini
untuk memperluas pengetahuan Anda tentang sistem rekomendasi dan menjelaskan
model berbeda yang digunakan
dalam rekomendasi, termasuk matriks
faktorisasi dan deep neural network.
Prasyarat
Kursus ini mengasumsikan bahwa Anda telah:
Menyelesaikan Kursus Singkat Machine Learning
baik secara langsung atau belajar mandiri, atau Anda memiliki pengetahuan yang setara.
Pemahaman tentang aljabar linear (produk dalam, produk matrix-vector).
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Informasi yang saya butuhkan tidak ada","missingTheInformationINeed","thumb-down"],["Terlalu rumit/langkahnya terlalu banyak","tooComplicatedTooManySteps","thumb-down"],["Sudah usang","outOfDate","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Masalah kode / contoh","samplesCodeIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2024-07-26 UTC."],[[["\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!*"]]
