H2O - Revision history

BPeat: Text replacement - "http:" to "https:"

2023-03-28T21:20:42Z

Text replacement - "http:" to "https:"

BPeat at 23:46, 7 December 2019

2019-12-07T23:46:49Z

BPeat at 20:47, 23 July 2019

2019-07-23T20:47:55Z

BPeat at 20:45, 23 July 2019

2019-07-23T20:45:37Z

BPeat at 02:40, 3 February 2019

2019-02-03T02:40:06Z

BPeat at 12:08, 5 January 2019

2019-01-05T12:08:37Z

BPeat at 07:39, 20 November 2018

2018-11-20T07:39:19Z

BPeat at 04:34, 28 September 2018

2018-09-28T04:34:53Z

BPeat at 04:31, 28 September 2018

2018-09-28T04:31:23Z

BPeat at 04:29, 28 September 2018

2018-09-28T04:29:10Z

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=H2O+Natural+language+processing+artificial+intelligence+deep+learning+simple YouTube search...]
+[https://www.youtube.com/results?search_query=H2O+Natural+language+processing+artificial+intelligence+deep+learning+simple YouTube search...]
-[http://www.google.com/search?q=H2O+Natural+language+processing+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=H2O+Natural+language+processing+deep+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Natural Language Processing (NLP)]]
 ** [[Natural Language Tools & Services]]
-* [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] - Intro + Interactive Hands-on Lab:
+* [https://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] - Intro + Interactive Hands-on Lab:
-** [http://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb Video]
+** [https://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb Video]
-** [http://www.slideshare.net/0xdata/driverless-ai-intro-handson-lab Slides]
+** [https://www.slideshare.net/0xdata/driverless-ai-intro-handson-lab Slides]
-** [http://h2oai.qwiklab.com Lab on AWS]
+** [https://h2oai.qwiklab.com Lab on AWS]
-* [http://aws.amazon.com/marketplace/pp/B07CVNXNG3 Pricing on AWS]
+* [https://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]
+* [https://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]
+* [https://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]
+* [https://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://blog.h2o.ai/category/driverless-ai/ Automatic Feature Engineering for Text Analytics – The Latest Addition to Our Kaggle Grandmasters’ Recipes]
+* [https://blog.h2o.ai/category/driverless-ai/ Automatic Feature Engineering for Text Analytics – The Latest Addition to Our Kaggle Grandmasters’ Recipes]
-* [http://www.slideshare.net/0xdata/the-making-of-a-realworld-moneyball?next_slideshow=1 The Making of a Real-World Moneyball]
+* [https://www.slideshare.net/0xdata/the-making-of-a-realworld-moneyball?next_slideshow=1 The Making of a Real-World Moneyball]
 * [[Graphical Tools for Modeling AI Components]]
-[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.
+[https://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment. [https://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 [https://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, [https://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 [https://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>
@@ Line 35: / Line 35: @@
 <youtube>irkV4sYExX4</youtube>
-http://dzone.com/storage/temp/9405669-automl.png
+https://dzone.com/storage/temp/9405669-automl.png
-http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/_images/DAI-architecture.png
+https://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/_images/DAI-architecture.png
-http://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/09/Screen-Shot-2018-09-11-at-20.12.50.png
+https://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/09/Screen-Shot-2018-09-11-at-20.12.50.png
-http://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/05/Screen-Shot-2018-05-04-at-16.23.26-e1525819147472.png
+https://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/05/Screen-Shot-2018-05-04-at-16.23.26-e1525819147472.png
-http://dzone.com/storage/temp/9405664-h2o.png
+https://dzone.com/storage/temp/9405664-h2o.png

@@ Line 20: / Line 20: @@
 * [http://blog.h2o.ai/category/driverless-ai/ Automatic Feature Engineering for Text Analytics – The Latest Addition to Our Kaggle Grandmasters’ Recipes]
 * [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. [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.

@@ Line 10: / Line 10: @@
 * [[Natural Language Processing (NLP)]]
 ** [[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://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.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>

@@ 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]
-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. 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.
 <youtube>niiibeHJtRo</youtube>

← Older revision		Revision as of 02:40, 3 February 2019
Line 1:		Line 1:
		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[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...]
		+	[http://www.google.com/search?q=H2O+Natural+language+processing+deep+machine+learning+ML+artificial+intelligence ...Google search]

	* [[Natural Language Processing (NLP)]]		* [[Natural Language Processing (NLP)]]

← Older revision		Revision as of 04:31, 28 September 2018
Line 29:		Line 29:
	http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/_images/DAI-architecture.png		http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/_images/DAI-architecture.png
	http://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/09/Screen-Shot-2018-09-11-at-20.12.50.png		http://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/09/Screen-Shot-2018-09-11-at-20.12.50.png
		+	http://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/05/Screen-Shot-2018-05-04-at-16.23.26-e1525819147472.png

@@ Line 24: / Line 24: @@
 <youtube>zBXXsA1-LOQ</youtube>
 <youtube>42Oo8TOl85I</youtube>
 http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/_images/DAI-architecture.png
 http://i2.wp.com/blog.h2o.ai/wp-content/uploads/2018/09/Screen-Shot-2018-09-11-at-20.12.50.png