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Opiniones y comentarios de aprendices correspondientes a Matrix Factorization and Advanced Techniques por parte de Universidad de Minnesota

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In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders....

Principales reseñas


2 de ene. de 2021

Really enjoyed the course!

One suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)


18 de jul. de 2017

great courses! They invite a lot of interviews to let me understand the sea of recommend system!

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26 - 27 de 27 revisiones para Matrix Factorization and Advanced Techniques

por Moustafa M

18 de abr. de 2020


9 de jun. de 2020