# Latent Dirichlet Allocation and Topic Modeling

An introductory notebook on using Amazon SageMaker to train and use LDA models.

<p align="center">
<img src="https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/lda_topic_modeling/img/img_documents.png">
<img src="https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/lda_topic_modeling/img/img_topics.png">
</p>

## References

The example used in these notebooks come from the following paper:

* Thomas Griffiths and Mark Steyvers. *Finding Scientific Topics.* Proceedings
  of the National Academy of Science, 101(suppl 1):5228-5235, 2004.

For more details about LDA and information about the algorithm used in the
Amazon SageMaker LDA algorithm consult the following papers:

* David Blei, Andrew Ng, and Michael Jordan. *Latent Dirichlet Allocation.*
  Journal of Machine Learning Research, 3(Jan):993-1022, 2003.
* Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham Kakade, and Matus Talgersy.
  *Tensor Decompositions for Learning Latent Variable Models.* Jounrla of
  Machine Learning Research, 15:2773-2832, 2014.
* Tamara Kolda and Brett Bader. *Tensor Decompositions and Applications.* SIAM
  REview, 51(3):455-500, 2009.