Difference between revisions of "TensorFlow"
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}} | }} | ||
[http://www.youtube.com/results?search_query=TensorFlow+deep+learning+artificial+intelligence Youtube search...] | [http://www.youtube.com/results?search_query=TensorFlow+deep+learning+artificial+intelligence Youtube search...] | ||
| − | [http://www.google.com/search?q=TensorFlow+deep+learning+artificial+intelligence+ML | + | [http://www.google.com/search?q=TensorFlow+deep+learning+artificial+intelligence+ML ...Google search] |
* [[Libraries & Frameworks]] | * [[Libraries & Frameworks]] | ||
* [[TensorFlow Overview & Tutorials]] | * [[TensorFlow Overview & Tutorials]] | ||
| − | * [[TensorFlow.js]] ... | + | * [[TensorFlow.js]] ... Browser and Node Server |
* [[TensorFlow Serving]] .. Cloud, On-prem | * [[TensorFlow Serving]] .. Cloud, On-prem | ||
* [[TensorFlow Lite]] ... Android, iOS, Raspberry Pi | * [[TensorFlow Lite]] ... Android, iOS, Raspberry Pi | ||
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* Related... | * Related... | ||
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** [[TFLearn]] | ** [[TFLearn]] | ||
** [[Swift]] | ** [[Swift]] | ||
| + | |||
| + | n TensorFlow 2.0, eager execution is enabled by default, with tight Keras integration. You can easily ingest datasets via tf.data pipelines, and you can monitor your training in TensorBoard directly from Colab and Jupyter Notebooks. [http://cloud.google.com/blog/products/ai-machine-learning/tensorflow-2-0-and-cloud-ai-make-it-easy-to-train-deploy-and-maintain-scalable-machine-learning-models TensorFlow 2.0 and Cloud AI make it easy to train, deploy, and maintain scalable machine learning models | Paige Bailey and Barrett Williams - Google] | ||
Revision as of 11:52, 7 April 2019
Youtube search... ...Google search
- Libraries & Frameworks
- TensorFlow Overview & Tutorials
- TensorFlow.js ... Browser and Node Server
- TensorFlow Serving .. Cloud, On-prem
- TensorFlow Lite ... Android, iOS, Raspberry Pi
- TensorBoard
- TensorFlow Playground
- We’re making tools and resources available so that anyone can use technology to solve problems | Google AI
n TensorFlow 2.0, eager execution is enabled by default, with tight Keras integration. You can easily ingest datasets via tf.data pipelines, and you can monitor your training in TensorBoard directly from Colab and Jupyter Notebooks. TensorFlow 2.0 and Cloud AI make it easy to train, deploy, and maintain scalable machine learning models | Paige Bailey and Barrett Williams - Google
