jerryliu112 Goto Github PK
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jerryliu112's Projects
amazon-redshift-utils
Amazon Redshift Utils contains utilities, scripts and view which are useful in a Redshift environment
amazon-sagemaker-examples
Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker
aws-ai-qna-bot
Code samples related to "Creating a Question and Answer Bot with Amazon Lex and Amazon Alexa", published on the AWS AI Blog. QnABot (pronounced “Q and A Bot”), uses Amazon Lex and Amazon Alexa to provide a conversational interface to your “Questions and Answers”, so users can just ask their questions and get quick and relevant answers.
aws-cloudformation-templates
A collection of useful CloudFormation templates
cheatsheets-ai
Essential Cheat Sheets for deep learning and machine learning researchers
data-science-your-way
Ways of doing Data Science Engineering and Machine Learning in R and Python
deeplearning-cfn
Distributed Deep Learning on AWS Using CloudFormation (CFN), MXNet and TensorFlow
gaming-analytics-pipeline
A methodology to allow customers to ingest, store, and analyze gameplay telemetry in near real-time.
keras-apache-mxnet
Amazon Deep Learning's Keras with Apache MXNet support
machine_learning_examples
A collection of machine learning examples and tutorials.
machinelearningnotebooks
Use the sample notebooks in this repo to explore the Azure Machine Learning service. Start with the 01.getting-started notebooks.
mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
models
Models and examples built with TensorFlow
neo4j-reco
Neo4j-based recommendation engine module with real-time and pre-computed recommendations.
recommendation
Recommendation System
sunburst-chart
A sunburst interactive chart web component for visualizing hierarchical data
tensorflow-white-paper-notes
Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation
visual-search
Visual search implementation resources, including an explanatory Jupyter notebook and Amazon SageMaker and AWS DeepLens code.
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