Difference between revisions of "H2O"

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{{#seo:
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|title=PRIMO.ai
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|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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[http://www.youtube.com/results?search_query=H2O+Natural+language+processing+artificial+intelligence+deep+learning+simple YouTube search...]
 
[http://www.youtube.com/results?search_query=H2O+Natural+language+processing+artificial+intelligence+deep+learning+simple YouTube search...]
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[http://www.google.com/search?q=H2O+Natural+language+processing+deep+machine+learning+ML+artificial+intelligence ...Google search]
  
* [[Natural Language Processing (NLP), Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)]]
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* [[Natural Language Processing (NLP)]]
* [[Natural Language Tools]]
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** [[Natural Language Tools & Services]]
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* [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] - Intro + Interactive Hands-on Lab:
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** [http://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb Video]
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** [http://www.slideshare.net/0xdata/driverless-ai-intro-handson-lab Slides]
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** [http://h2oai.qwiklab.com Lab on AWS]
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* [http://aws.amazon.com/marketplace/pp/B07CVNXNG3 Pricing on AWS]
 
* [http://www.h2o.ai/products/h2o-driverless-ai/ Natural Language Processing (NLP) in H2O Driverless AI]
 
* [http://www.h2o.ai/products/h2o-driverless-ai/ Natural Language Processing (NLP) in H2O Driverless AI]
 
* [http://www.brighttalk.com/webcast/16463/335544? Catch-up with the latest in Driverless AI]
 
* [http://www.brighttalk.com/webcast/16463/335544? Catch-up with the latest in Driverless AI]
 
* [http://www.prnewswire.com/news-releases/h2oai-expands-driverless-ai-to-new-class-of-use-cases-with-natural-language-processing-300711366.html H2O.ai Expands Driverless AI to New Class of Use Cases with Natural Language Processing]
 
* [http://www.prnewswire.com/news-releases/h2oai-expands-driverless-ai-to-new-class-of-use-cases-with-natural-language-processing-300711366.html H2O.ai Expands Driverless AI to New Class of Use Cases with Natural Language Processing]
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* [http://blog.h2o.ai/category/driverless-ai/ Automatic Feature Engineering for Text Analytics – The Latest Addition to Our Kaggle Grandmasters’ Recipes]
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* [http://www.slideshare.net/0xdata/the-making-of-a-realworld-moneyball?next_slideshow=1 The Making of a Real-World Moneyball]
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* [[Graphical Tools for Modeling AI Components]]
  
http://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/09/Screen-Shot-2018-09-11-at-20.12.50.png?
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[http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment. [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] turns Kaggle-winning recipes into production-ready code and is specifically designed to avoid common mistakes such as under or overfitting, data leakage or improper model validation. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy. With [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI], everyone can now train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client/server API through a variety of languages such as [[Python]], Java, C++, go, C# and many more. To speed up training, [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] uses highly optimized C++/CUDA algorithms to take full advantage of the latest compute hardware. For example, Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU supercomputers on Intel and IBM platforms, both in the cloud or on-premise. There are two more product innovations in [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI]: statistically rigorous automatic data visualization and interactive model interpretation with reason codes and explanations in plain English. Both help data scientists and analysts to quickly validate the data and models.
  
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Revision as of 19:46, 7 December 2019

YouTube search... ...Google search

Driverless AI speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment. Driverless AI turns Kaggle-winning recipes into production-ready code and is specifically designed to avoid common mistakes such as under or overfitting, data leakage or improper model validation. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy. With Driverless AI, everyone can now train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client/server API through a variety of languages such as Python, Java, C++, go, C# and many more. To speed up training, Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage of the latest compute hardware. For example, Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU supercomputers on Intel and IBM platforms, both in the cloud or on-premise. There are two more product innovations in Driverless AI: statistically rigorous automatic data visualization and interactive model interpretation with reason codes and explanations in plain English. Both help data scientists and analysts to quickly validate the data and models.

9405669-automl.png DAI-architecture.png Screen-Shot-2018-09-11-at-20.12.50.png Screen-Shot-2018-05-04-at-16.23.26-e1525819147472.png 9405664-h2o.png