Nicolas Hug
ML Engineer
Tags
matrix factorization PCA SVD surprise recommender systems pygbm gradient boosting decision trees Gradient boosting Cython Python Decision Trees Partial Dependence Plots ffcv pytorch#matrix factorization
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Understanding matrix factorization for recommendation (part 1) - preliminary insights on PCA
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Understanding matrix factorization for recommendation (part 2) - the model behind SVD
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Understanding matrix factorization for recommendation (part 3) - SVD for recommendation
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Understanding matrix factorization for recommendation (part 4) - algorithm implementation
#PCA
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Understanding matrix factorization for recommendation (part 1) - preliminary insights on PCA
-
Understanding matrix factorization for recommendation (part 2) - the model behind SVD
-
Understanding matrix factorization for recommendation (part 3) - SVD for recommendation
-
Understanding matrix factorization for recommendation (part 4) - algorithm implementation
#SVD
-
Understanding matrix factorization for recommendation (part 1) - preliminary insights on PCA
-
Understanding matrix factorization for recommendation (part 2) - the model behind SVD
-
Understanding matrix factorization for recommendation (part 3) - SVD for recommendation
-
Understanding matrix factorization for recommendation (part 4) - algorithm implementation
#surprise
#recommender systems
#pygbm
#gradient boosting decision trees
#Gradient boosting
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Understanding Gradient Boosting as a gradient descent
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Around gradient boosting: classification, missing values, second order derivatives, and line search.