Deep Learning and Graph Databases
Deep Learning and Graph Databases
Deep Learning and Graph Databases

This book is 10% complete

Last updated on 2019-04-07

About the Book

I believe the the path to real Artificial General Intelligence (AGI) will depend on deep learning, symbolic AI, probabilistic programming, and knowledge representation. While I cover deep learning, probabilistic graph models, probabilistic logic programming, and general AI programming in my book Python Intelligent Systems, in this book I drill down into using deep learning Natural Language Processing (NLP) to help automate the creation of Knowledge Graphs, use deep learning embeddings to create and maintain additional relationships in Knowledge Graphs, and integrating both deep learning and Knowledge Graphs in larger systems.

About the Author

Mark Watson
Mark Watson

Mark Watson a consultant specializing in deep learning, machine learning, and general artificial intelligence software development. He uses Python, Common Lisp, Java, Clojure, Haskell, and Ruby for development.

He is the author of 23 published books on Artificial Intelligence, Deep Learning, Java, Ruby, Machine Learning, Common LISP, Clojure, JavaScript, Semantic Web, NLP, C++, Linux, and Scheme.

Mark's consulting customer list includes: Google, Capital One, CompassLabs, Disney, Sitescout.com, Embed.ly, and Webmind Corporation.

web site:  https://markwatson.com/

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