Skip to content

Commit a0e2c74

Browse files
committed
Added: Gluon recommender system notebook
1 parent 9bd5fa3 commit a0e2c74

File tree

4 files changed

+1219
-0
lines changed

4 files changed

+1219
-0
lines changed

README.md

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -14,6 +14,7 @@ These examples provide a gentle introduction to machine learning concepts as the
1414
- [Cancer Prediction](introduction_to_applying_machine_learning/breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
1515
- [Ensembling](introduction_to_applying_machine_learning/ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.
1616
- [Video Game Sales](introduction_to_applying_machine_learning/video_game_sales) develops a binary prediction model for the success of video games based on review scores.
17+
- [MXNet Gluon Recommender System](introduction_to_applying_machine_learning/gluon_recommender_system) uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews.
1718

1819
### Introduction to Amazon Algorithms
1920

introduction_to_applying_machine_learning/README.md

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -10,3 +10,4 @@ These examples provide a gentle introduction to machine learning concepts as the
1010
- [Cancer Prediction](breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
1111
- [Ensembling](ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.
1212
- [Video Game Sales](video_game_sales) develops a binary prediction model for the success of video games based on review scores.
13+
- [MXNet Gluon Recommender System](gluon_recommender_system) uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews.

0 commit comments

Comments
 (0)