Difference between revisions of "Keras"
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*[[TensorFlow]] | *[[TensorFlow]] | ||
*[http://keras.io/ Keras: The Python Deep Learning library] | *[http://keras.io/ Keras: The Python Deep Learning library] | ||
| − | * [http://transcranial.github.io/keras-js/#/ Keras.js] runs Keras models in the browser, with GPU support using WebGL. since Keras uses a number of frameworks as backends, the models can be trained in TensorFlow, CNTK, and other frameworks as well. | + | *[[Keras.js]] |
| + | *[http://transcranial.github.io/keras-js/#/ Keras.js] runs Keras models in the browser, with GPU support using WebGL. since Keras uses a number of frameworks as backends, the models can be trained in TensorFlow, CNTK, and other frameworks as well. | ||
**[[TensorFlow.js]] for training and deploying ML models in the browser and on Node.js (was called Deeplearnjs) | **[[TensorFlow.js]] for training and deploying ML models in the browser and on Node.js (was called Deeplearnjs) | ||
*[http://medium.com/applied-data-science/how-to-build-your-own-alphazero-ai-using-python-and-keras-7f664945c188 How to build your own AlphaZero AI using Python and Keras] | *[http://medium.com/applied-data-science/how-to-build-your-own-alphazero-ai-using-python-and-keras-7f664945c188 How to build your own AlphaZero AI using Python and Keras] | ||
Revision as of 21:10, 7 August 2018
- TensorFlow
- Keras: The Python Deep Learning library
- Keras.js
- Keras.js runs Keras models in the browser, with GPU support using WebGL. since Keras uses a number of frameworks as backends, the models can be trained in TensorFlow, CNTK, and other frameworks as well.
- TensorFlow.js for training and deploying ML models in the browser and on Node.js (was called Deeplearnjs)
- How to build your own AlphaZero AI using Python and Keras
- Transfer Learning With Keras
- Git - GitHub and GitLab
YouTube Keras Series
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Use Keras if you need a deep learning library that:
- Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
- Supports both convolutional networks and recurrent networks, as well as combinations of the two.
- Runs seamlessly on CPU and GPU.
