# Machine Learning

## Rescources for Neural Networks

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  * machine learning cheatsheet [https://stanford.edu/\~shervine/teaching/cs-229/](https://stanford.edu/~shervine/teaching/cs-229/)
* deep learning cheatsheet [https://stanford.edu/\~shervine/teaching/cs-230/](https://stanford.edu/~shervine/teaching/cs-230/)
* lecture notes on CNN [http://cs231n.github.io/](http://cs231n.github.io/)
* videos on neural networks: [https://www.youtube.com/watch?v=aircAruvnKk\&list=PLZHQObOWTQDNU6R1\_67000Dx\_ZCJB-3pi](https://www.youtube.com/watch?v=aircAruvnKk\&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
* run jupyter notebooks online, ML tutorials [https://notebooks.azure.com/](https://notebooks.azure.com/)
* projects using deep learning for arts [https://magenta.tensorflow.org/demos](https://magenta.tensorflow.org/demos)
* simple platform for trying out machine learning models [https://runwayml.com](https://runwayml.com)
* [What are the Hyperparameters of a NNet and how to tune them](https://bit.ly/2qRArI)
* [The vanishing gradient problem in NNets](https://bit.ly/2Pq03qk)
* [Understanding the Vanishing Gradient problem (video )](https://github.com/cerkut/machine-learning-workstation-docs/blob/master/useful-links/machine-learning/%20https:/youtu.be/SKMpmAOUa2Q/README.md)
* [Transfer Learning Tutorial](https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a)
* [Transfer Learning Tutorial (Simple with Example)](https://towardsdatascience.com/keras-transfer-learning-for-beginners-6c9b8b7143e)
* [Keras pre-trained models](https://keras.io/applications/)
* [Understanding Loss Functions](https://medium.com/deep-learning-demystified/loss-functions-explained-3098e8ff2b27)
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