Natural language or text is regularly voted as the most important data type after structured data. This is because text can carry an enormous amount of hidden insight. In this session, we show you how to get started with the natural language processing (NLP) techniques using Amazon SageMaker, a platform to easily build, train, and deploy models at scale. We review all of the NLP-related algorithms available on Amazon SageMaker, including Amazon algorithms (Latent Dirichlet Allocation (LDA), Neural Topic Model (NTM), and Sequence2Sequence (seq2seq), and BlazingText). We dive into the details, and we run through an example Jupyter Notebook that uses these algorithms.

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Resolving NLP Problems Using
Amazon SageMaker Algorithms
Binoy Das
Partner Solutions Architect
AWS
G P S C T 3 0 5
Chris Burns
Partner Solutions Architect
AWS

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Agenda
NLP―Background and current state
Neural Networks
Introduction to Amazon SageMaker
Introduction to Amazon SageMaker algorithms
Sample repositories

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Cognitive simulation
“Since a human being using and understanding a
sentence in a natural language requires an implicit
knowledge of the sentence's context- dependent use,
the only way to make a computer that could
understand and translate a natural language may well
be, as Turing suspected, to program it to learn about
the world.”
Hubert L. Dreyfus

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NLP use cases
Text classification
Document summarization
Language modeling
Caption generation
Speech recognition
Machine translation
Question answering

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Typical Neural Networks

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Recurrent Neural Network

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RNN―Unfolded

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Long Short-Term Memory

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Amazon SageMaker
1 2 3 4
I I I I
Notebook Instances Algorithms ML Training Service ML Hosting Service

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Algorithm Type
Image Classification Image
Clustering Structured Data
Random Cut Forest Structured Data
Linear Learner Structured Data
Factorization Machines Structured Data
XGBoost Structured Data
PCA and K-Means Structured Data
DeepAR TimeSeries
Latent Dirichlet Allocation Natural Language
Blazing Text Natural Language
Sequence2Sequence Natural Language
Neural Topic Modeling Natural Language

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BlazingText―Word2vec

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BlazingText―Classification
Preprocessing
Transform to
Word Vectors
Classifier
BlazingText Classifier

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Sequence to Sequence

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Topic Modeling―LDA and Neural Topic Modeling

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https://github.com/aws-samples/aws-nlp-workshop

21. Thank you!
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