Add LDA to introduction notebook README list

The list of algorithms is also rearranged in alphabetical order to match the
order shown in GitHub.

Author: Chris Swierczewski

introduction_to_amazon_algorithms/README.md

@@ -3,9 +3,10 @@
 This directory includes introductory examples to Amazon SageMaker Algorithms that we have developed so far.  It seeks to provide guidance and examples on basic functionality rather than a detailed scientific review or an implementation on complex, real-world data.
 
 Example Notebooks include:
-- *linear_mnist*: Predicts whether a handwritten digit from the MNIST dataset is a 0 or not using a binary classifier from Amazon SageMaker Linear Learner.
 - *factorization_machines_mnist*: Predicts whether a handwritten digit from the MNIST dataset is a 0 or not using a binary classifier from Amazon SageMaker Factorization Machines.
-- *pca_mnist*: Uses Amazon SageMaker Principal Components Analysis (PCA) to calculate eigendigits from MNIST.
+- *lda_topic_modeling*: Topic modeling using Amazon SageMaker Latent Dirichlet Allocation (LDA) on a synthetic dataset.
+- *linear_mnist*: Predicts whether a handwritten digit from the MNIST dataset is a 0 or not using a binary classifier from Amazon SageMaker Linear Learner.
 - *ntm_synthetic*: Uses Amazon SageMaker Neural Topic Model (NTM) to uncover topics in documents from a synthetic data source, where topic distributions are known.
-- *xgboost_mnist*: Uses Amazon SageMaker XGBoost to classifiy handwritten digits from the MNIST dataset into one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented. 
+- *pca_mnist*: Uses Amazon SageMaker Principal Components Analysis (PCA) to calculate eigendigits from MNIST.
 - *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 XGBoost.
+- *xgboost_mnist*: Uses Amazon SageMaker XGBoost to classifiy handwritten digits from the MNIST dataset into one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented.