Difference between revisions of "H2O"
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* [[Natural Language Processing (NLP)]] | * [[Natural Language Processing (NLP)]] | ||
** [[Natural Language Tools & Services]] | ** [[Natural Language Tools & Services]] | ||
| − | * Driverless AI - Intro + Interactive Hands-on Lab: | + | * [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] - Intro + Interactive Hands-on Lab: |
** [http://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb Video] | ** [http://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb Video] | ||
** [http://www.slideshare.net/0xdata/driverless-ai-intro-handson-lab Slides] | ** [http://www.slideshare.net/0xdata/driverless-ai-intro-handson-lab Slides] | ||
| Line 21: | Line 21: | ||
* [http://www.slideshare.net/0xdata/the-making-of-a-realworld-moneyball?next_slideshow=1 The Making of a Real-World Moneyball] | * [http://www.slideshare.net/0xdata/the-making-of-a-realworld-moneyball?next_slideshow=1 The Making of a Real-World Moneyball] | ||
| − | [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. 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. | + | [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. |
<youtube>niiibeHJtRo</youtube> | <youtube>niiibeHJtRo</youtube> | ||
Revision as of 16:47, 23 July 2019
YouTube search... ...Google search
- Natural Language Processing (NLP)
- Driverless AI - Intro + Interactive Hands-on Lab:
- Pricing on AWS
- Natural Language Processing (NLP) in H2O Driverless AI
- Catch-up with the latest in Driverless AI
- H2O.ai Expands Driverless AI to New Class of Use Cases with Natural Language Processing
- Automatic Feature Engineering for Text Analytics – The Latest Addition to Our Kaggle Grandmasters’ Recipes
- The Making of a Real-World Moneyball
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.
