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My solution for the fifth project in the Udacity Deep-Learning Nanodegree. A recurrent neural network is trained for the task of analyzing sentiments. After training the model gets deployed and is used in a web app.

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Udacity-Deployment-Project

The Jupyter-Notebook-File SageMaker_Project.ipynb includes my solution to the Deploying-a-Sentiment-Analysis-Model-Project in the Deeplearning-Nanodegree.

Description

A recurrent neural network (RNN) with LSTM-cells is built to decide whether a given movie review is positive or negative. For this task the RNN is trained with the IMDb dataset. The model is built and deployed with the cloud machine-learning platform Amazon SageMaker. After deployment a Lambda function and an API Gateway is set up to access the trained RNN-model with a simple Web-App.

The Web-App can be started by opening the index.html-file in a browser.

Since the Lambda-function and the API-Gateway are deleted now, it is not possible to access the trained RNN-model with the Web-App right now.

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My solution for the fifth project in the Udacity Deep-Learning Nanodegree. A recurrent neural network is trained for the task of analyzing sentiments. After training the model gets deployed and is used in a web app.

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