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Te damos la bienvenida a Sistemas de recomendación. Diseñamos este curso
para ampliar tus conocimientos
sobre sistemas de recomendación y explicar
modelos diferentes usados en la recomendación, incluida la
la factorización y las redes neuronales profundas.
Requisitos previos
En este curso, se supone que ya cuentas con los siguientes conocimientos:
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Falta la información que necesito","missingTheInformationINeed","thumb-down"],["Muy complicado o demasiados pasos","tooComplicatedTooManySteps","thumb-down"],["Desactualizado","outOfDate","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Problema con las muestras o los códigos","samplesCodeIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2024-07-26 (UTC)"],[[["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."]]],[]]
