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Mining of Massive Datasets Resúmenes (1.º edición)
Jure Leskovec, Anand Rajaraman - ISBN: 9781107077232
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Ver todos los 2 resúmenes de Mining of Massive Datasets, escritos por Jure Leskovec, Anand Rajaraman. Los resúmenes de Mining of Massive Datasets en Stuvia están escritos por estudiantes o profesores, lo que facilita y acelera la comprensión del contenido del libro de texto. Encontrar el resumen que se adapte perfectamente a tu estilo de aprendizaje hará que estudiar sea mucho más fácil.
Resúmenes más vendidos de Mining of Massive Datasets
A summary of the Articles of the Subject Customer Analytics, up to Collaborative Filtering Cross-selling and Upselling. Also Including the recommended webclips. The names are mentioned below. Use Big Data to Create Value for Customers, Not Just Target Them. - Niraj Dawer. 2016 Quick profits with RFM analysis - Arthur Middleton Hughers, 2018 Clip: Clumpiness and Customer Lifetime Value - Knowledge at Whatron Big Data: New Tricks for econometrics - Hal R. Varian, Journal of economic perspective...
- Resumen
- • 20 páginas •
A summary of the Articles of the Subject Customer Analytics, up to Collaborative Filtering Cross-selling and Upselling. Also Including the recommended webclips. The names are mentioned below. Use Big Data to Create Value for Customers, Not Just Target Them. - Niraj Dawer. 2016 Quick profits with RFM analysis - Arthur Middleton Hughers, 2018 Clip: Clumpiness and Customer Lifetime Value - Knowledge at Whatron Big Data: New Tricks for econometrics - Hal R. Varian, Journal of economic perspective...
Summary of the theory for Advances in Data Mining for the Computer Science master at Leiden University. Covers tokenization, Recommender Systems (Naive methods, Collaborative filtering, Matrix Factorization), RBM, Similarity Search (shingling, minhashing, LSH) and Pagerank (Markov process, RageRank algorithm) It is missing the part about Support Vector machines, the last bit of minhasing and the part of the DGIM algorithm for data streams and. Parts missing are labeled with a colored marking.
- Resumen
- • 38 páginas •
Summary of the theory for Advances in Data Mining for the Computer Science master at Leiden University. Covers tokenization, Recommender Systems (Naive methods, Collaborative filtering, Matrix Factorization), RBM, Similarity Search (shingling, minhashing, LSH) and Pagerank (Markov process, RageRank algorithm) It is missing the part about Support Vector machines, the last bit of minhasing and the part of the DGIM algorithm for data streams and. Parts missing are labeled with a colored marking.
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Resúmenes más recientes de Mining of Massive Datasets
A summary of the Articles of the Subject Customer Analytics, up to Collaborative Filtering Cross-selling and Upselling. Also Including the recommended webclips. The names are mentioned below. Use Big Data to Create Value for Customers, Not Just Target Them. - Niraj Dawer. 2016 Quick profits with RFM analysis - Arthur Middleton Hughers, 2018 Clip: Clumpiness and Customer Lifetime Value - Knowledge at Whatron Big Data: New Tricks for econometrics - Hal R. Varian, Journal of economic perspective...
- Resumen
- • 20 páginas •
A summary of the Articles of the Subject Customer Analytics, up to Collaborative Filtering Cross-selling and Upselling. Also Including the recommended webclips. The names are mentioned below. Use Big Data to Create Value for Customers, Not Just Target Them. - Niraj Dawer. 2016 Quick profits with RFM analysis - Arthur Middleton Hughers, 2018 Clip: Clumpiness and Customer Lifetime Value - Knowledge at Whatron Big Data: New Tricks for econometrics - Hal R. Varian, Journal of economic perspective...
Summary of the theory for Advances in Data Mining for the Computer Science master at Leiden University. Covers tokenization, Recommender Systems (Naive methods, Collaborative filtering, Matrix Factorization), RBM, Similarity Search (shingling, minhashing, LSH) and Pagerank (Markov process, RageRank algorithm) It is missing the part about Support Vector machines, the last bit of minhasing and the part of the DGIM algorithm for data streams and. Parts missing are labeled with a colored marking.
- Resumen
- • 38 páginas •
Summary of the theory for Advances in Data Mining for the Computer Science master at Leiden University. Covers tokenization, Recommender Systems (Naive methods, Collaborative filtering, Matrix Factorization), RBM, Similarity Search (shingling, minhashing, LSH) and Pagerank (Markov process, RageRank algorithm) It is missing the part about Support Vector machines, the last bit of minhasing and the part of the DGIM algorithm for data streams and. Parts missing are labeled with a colored marking.
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