Difference between revisions of "Keras"
| Line 12: | Line 12: | ||
* [[Keras.js]] | * [[Keras.js]] | ||
* [[Auto Keras]] | * [[Auto Keras]] | ||
| − | * [http://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf | + | * [https://www.datacamp.com/community/blog/keras-cheat-sheet Keras_Cheat_Sheet_Python - Data Camp ] [http://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf pdf] |
* [[TensorFlow.js]] for training and deploying ML models in the browser and on [[Javascript#Node.js|Node.js]] (was called Deeplearnjs) | * [[TensorFlow.js]] for training and deploying ML models in the browser and on [[Javascript#Node.js|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] | ||
| Line 34: | Line 34: | ||
* Runs seamlessly on CPU and GPU. | * Runs seamlessly on CPU and GPU. | ||
| + | <youtube>UYRBHFAvLSs</youtube> | ||
<youtube>j_pJmXJwMLA</youtube> | <youtube>j_pJmXJwMLA</youtube> | ||
<youtube>f6Bf3gl4hWY</youtube> | <youtube>f6Bf3gl4hWY</youtube> | ||
Revision as of 09:36, 31 August 2019
Youtube search... ...Google search
- TensorFlow
- Keras: The Python Deep Learning library
- Keras.js
- Auto Keras
- Keras_Cheat_Sheet_Python - Data Camp pdf
- 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
- NLP Keras model in browser with TensorFlow.js
- Transfer Learning With Keras
- Git - GitHub and GitLab
Note: Keras capability is now also part of Tensorflow
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.
YouTube Keras Series
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.
