Add Object2Vec notebook examples (aws#461)

Author: pnpnpn

README.md

@@ -46,6 +46,9 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [DeepAR for time series forecasting](introduction_to_amazon_algorithms/deepar_synthetic) illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set.
 - [BlazingText Word2Vec](introduction_to_amazon_algorithms/blazingtext_word2vec_text8) generates Word2Vec embeddings from a cleaned text dump of Uncyclopedia articles using SageMaker's fast and scalable BlazingText implementation.
 - [Object Detection](introduction_to_amazon_algorithms/object_detection_pascalvoc_coco) illustrates how to train an object detector using the Amazon SageMaker Object Detection algorithm with different input formats (RecordIO and image).
+- [Object2Vec for movie recommendation](introduction_to_amazon_algorithms/object2vec_movie_recommendation) demonstrates how Object2Vec can be used to model data consisting of pairs of singleton tokens using movie recommendation as a running example.
+- [Object2Vec for multi-label classification](introduction_to_amazon_algorithms/object2vec_multilabel_genre_classification) shows how ObjectToVec algorithm can train on data consisting of pairs of sequences and singleton tokens using the setting of genre prediction of movies based on their plot descriptions.
+- [Object2Vec for sentence similarity](introduction_to_amazon_algorithms/object2vec_sentence_similarity) explains how to train Object2Vec using sequence pairs as input using sentence similarity analysis as the application.
 
 ### Scientific Details of Algorithms
 

introduction_to_amazon_algorithms/README.md

@@ -16,3 +16,6 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [XGBoost for multi-class classification](xgboost_mnist) uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to classifiy handwritten digits from the MNIST dataset as one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented.
 - [DeepAR for time series forecasting](deepar_synthetic) illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set.
 - [BlazingText Word2Vec](blazingtext_word2vec_text8) generates Word2Vec embeddings from a cleaned text dump of Uncyclopedia articles using SageMaker's fast and scalable BlazingText implementation.
+- [Object2Vec for movie recommendation](object2vec_movie_recommendation) demonstrates how Object2Vec can be used to model data consisting of pairs of singleton tokens using movie recommendation as a running example.
+- [Object2Vec for multi-label classification](object2vec_multilabel_genre_classification) shows how ObjectToVec algorithm can train on data consisting of pairs of sequences and singleton tokens using the setting of genre prediction of movies based on their plot descriptions.
+- [Object2Vec for sentence similarity](object2vec_sentence_similarity) explains how to train Object2Vec using sequence pairs as input using sentence similarity analysis as the application.