Arpin readme link fix

README.md

@@ -1,6 +1,6 @@
 # Amazon SageMaker Examples
 
-This repository contains example notebooks that show how to apply machine learning and deep learning in [Amazon SageMaker](https://aws.amazon.com/machine-learning/platforms/sagemaker).
+This repository contains example notebooks that show how to apply machine learning and deep learning in [Amazon SageMaker](https://aws.amazon.com/sagemaker)
 
 ## Examples
 
@@ -25,6 +25,7 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [Neural Topic Model (NTM)](introduction_to_amazon_algorithms/ntm_synthetic) uses Amazon SageMaker Neural Topic Model (NTM) to uncover topics in documents from a synthetic data source, where topic distributions are known.
 - [Principal Components Analysis (PCA)](introduction_to_amazon_algorithms/pca_mnist) uses Amazon SageMaker PCA to calculate eigendigits from MNIST.
 - [Seq2Seq](introduction_to_amazon_algorithms/seq2seq) uses the Amazon SageMaker Seq2Seq algorithm that's built on top of [Sockeye](https://github.com/awslabs/sockeye), which is a sequence-to-sequence framework for Neural Machine Translation based on MXNet.  Seq2Seq implements state-of-the-art encoder-decoder architectures which can also be used for tasks like Abstractive Summarization in addition to Machine Translation.  This notebook shows translation from English to German text.
+- [Image Classification](introduction_to_amazon_algorithms/imageclassification_caltech) includes full training and transfer learning examples of Amazon SageMaker's Image Classification algorithm.  This uses a ResNet deep convolutional neural network to classify images from the caltech dataset.
 - [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 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.
 

introduction_to_amazon_algorithms/README.md

@@ -11,5 +11,6 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [Neural Topic Model (NTM)](ntm_synthetic) uses Amazon SageMaker Neural Topic Model (NTM) to uncover topics in documents from a synthetic data source, where topic distributions are known.
 - [Principal Components Analysis (PCA)](pca_mnist) uses Amazon SageMaker PCA to calculate eigendigits from MNIST.
 - [Seq2Seq](seq2seq) uses the Amazon SageMaker Seq2Seq algorithm that's built on top of [Sockeye](https://github.com/awslabs/sockeye), which is a sequence-to-sequence framework for Neural Machine Translation based on MXNet.  Seq2Seq implements state-of-the-art encoder-decoder architectures which can also be used for tasks like Abstractive Summarization in addition to Machine Translation.  This notebook shows translation from English to German text.
+- [Image Classification](introduction_to_amazon_algorithms/imageclassification_caltech) includes full training and transfer learning examples of Amazon SageMaker's Image Classification algorithm.  This uses a ResNet deep convolutional neural network to classify images from the caltech dataset.
 - [XGBoost for regression](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](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.