Merge pull request #139 from channy/master

Fix some typos including Python SDDK

Author: djarpin

README.md

@@ -27,7 +27,7 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [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.
+- [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.
 
 ### Scientific Details of Algorithms
 
@@ -49,7 +49,7 @@ These examples that showcase unique functionality available in Amazon SageMaker.
 - [Installing the R Kernel](advanced_functionality/install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance.
 - [Bring Your Own scikit Algorithm](advanced_functionality/scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.
 
-### Amazon SageMaker TensorFlow and MXNet Pre-Built Containers and the Python SDDK
+### Amazon SageMaker TensorFlow and MXNet Pre-Built Containers and the Python SDK
 
 These examples focus on the Amazon SageMaker Python SDK which allows you to write idiomatic TensorFlow or MXNet and then train or host in pre-built containers.
 

sagemaker-python-sdk/README.md

@@ -1,6 +1,6 @@
 # Amazon SageMaker Examples
 
-### Amazon SageMaker TensorFlow and MXNet Pre-Built Containers and the Python SDDK
+### Amazon SageMaker TensorFlow and MXNet Pre-Built Containers and the Python SDK
 
 These examples focus on the Amazon SageMaker Python SDK which allows you to write idiomatic TensorFlow or MXNet and then train or host in pre-built containers.