Updated: README with BlazingText and videogames to use prefix #174
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lukmis
reviewed
Feb 2, 2018
README.md
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| @@ -30,6 +30,7 @@ These examples provide quick walkthroughs to get you up and running with Amazon | |||
| - [XGBoost for regression](introduction_to_amazon_algorithms/xgboost_abalone) predicts the age of abalone ([Abalone dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html)) using regression from Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost). | |||
| - [XGBoost for multi-class classification](introduction_to_amazon_algorithms/xgboost_mnist) uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to classify 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](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 large, cleaned text dump of Wikipedia articles using SageMaker's fast and scalable BlazingText implementation. | |||
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The notebook says "small preprocessed dataset" (100MB) so please remove 'large' from description here. Potentially reword 'fast' into 'efficient' to match notebook.
lukmis
reviewed
Feb 2, 2018
| - [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 large, cleaned text dump of Wikipedia articles using SageMaker's fast and scalable BlazingText implementation. |
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Please update when updating the other place.
lukmis
reviewed
Feb 2, 2018
README.md
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| These examples show how to use Amazon SageMaker for model training, hosting, and inference through Apache Spark using [SageMaker Spark](https://github.com/aws/sagemaker-spark). SageMaker Spark allows you to interleave Spark Pipeline stages with Pipeline stages that interact with Amazon SageMaker. | ||
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| - [MNIST with SageMaker Spark](sagemaker-spark/pyspark_mnist) |
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Please update the link to use PySpark since the examples are pyspark.
lukmis
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Feb 2, 2018
atqy
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Aug 16, 2022
Update MX NB per bug bash testing
atqy
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to atqy/amazon-sagemaker-examples
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Aug 16, 2022
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Minor update to README to add a line for the new BlazingText algorithm.
I also slipped in a fix for the video game sales notebook based on this issue. I tested the video game notebook. It required a manual setup anyway, so as long as that direction is followed, it runs successfully on a SageMaker Notebook Instance.