Difference between revisions of "Google"

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* [http://codelabs.developers.google.com/ Google Developers Codelabs]
 
* [http://codelabs.developers.google.com/ Google Developers Codelabs]
 
* [[TensorFlow]]  
 
* [[TensorFlow]]  
* [http://www.tensorflow.org/hub TensorFlow Hub] is a library for reusable machine learning modules. TensorFlow Hub provides a library of TensorFlow modules that you can use to speed up the process of training your model.  
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* [http://www.tensorflow.org/hub TensorFlow Hub] is a library for reusable machine learning modules that you can use to speed up the process of training your model. A [[TensorFlow]] module is a reusable piece of a [[TensorFlow]] graph. With transfer learning, you can use [[TensorFlow]] modules to preprocess input feature vectors, or you can incorporate a [[TensorFlow]] module into your model as a trainable layer. This can help you train your model faster, using a smaller dataset, while improving generalization.
* [[AI Hub]] On the AI Hub, you can explore and use a variety of AI asset categories.
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* [[AI Hub]] - explore and use a variety of AI asset categories. [[AI Hub]] offers a collection of components for developers and data scientists building artificial intelligence (AI) systems. Use [[AI Hub]] to: [1]
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Find, deploy, and use [[Kubeflow Pipelines]] and components, [2] explore code and learn in interactive [[Jupyter]] notebooks, [3] explore and reuse [[TensorFlow]] modules, Explore, deploy, and use trained models, [4] use prepackaged virtual machine (VM) images to quickly set up your AI environment, and [5] share AI components with your colleagues.
 
* [http://deepmind.com/research/open-source/ DeepMind - Open Source]
 
* [http://deepmind.com/research/open-source/ DeepMind - Open Source]
 
** [[Google DeepMind AlphaGo Zero]]
 
** [[Google DeepMind AlphaGo Zero]]

Revision as of 18:12, 2 October 2019

YouTube search... ...Google search

Find, deploy, and use Kubeflow Pipelines and components, [2] explore code and learn in interactive Jupyter notebooks, [3] explore and reuse TensorFlow modules, Explore, deploy, and use trained models, [4] use prepackaged virtual machine (VM) images to quickly set up your AI environment, and [5] share AI components with your colleagues.

The Path From Cloud AutoML to Custom Model (Cloud Next '19)

by Sara Robinson

D0Vr_uJXQAAAfIo.jpg