Nicolas Hug
ML Engineer
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matrix factorization
PCA
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surprise
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#matrix factorization
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
#PCA
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
Surprise, a Python scikit for building and analyzing recommender systems
#recommender systems
Surprise, a Python scikit for building and analyzing recommender systems
#pygbm
Pygbm, a fast pure-Python implementation of Gradient Boosting Decision Trees
#gradient boosting decision trees
Pygbm, a fast pure-Python implementation of Gradient Boosting Decision Trees
#Gradient boosting
Understanding Gradient Boosting as a gradient descent
Around gradient boosting: classification, missing values, second order derivatives, and line search.
#Cython
Cython notes and tips
#Python
Cython notes and tips
#Decision Trees
Efficient Partial Dependence Plots with decision trees
#Partial Dependence Plots
Efficient Partial Dependence Plots with decision trees
#ffcv
Deep dive into FFCV internals
#pytorch
Deep dive into FFCV internals
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