# Resources for Machine Learning & Deep Learning


* [Tensorflow examples](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/TensorFlow): examples in TF.
* [Caffe examples](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Caffe): examples in Caffe.
* [DeepArt examples](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Numpy): deep learning generated art.
* [ML Notebooks](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Notebooks): jupyter notebooks with ML examples.
* [Numpy examples](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Numpy): some snippets in Numpy.


## Learning Resources


### Introductory Courses

* [Stanford's Machine Learning Course](http://cs229.stanford.edu/)
* [Google's Developer Machine Learning Course](https://developers.google.com/machine-learning)


### Deep Learning


* [A Chart of Neural Networks](http://www.asimovinstitute.org/neural-network-zoo/).
* [UCL Course on RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
* [Stanford's Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/)
* [The 9 CNN Papers You Need To Know About](https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html).
* [NVIDIA Deep Learning Course](https://www.youtube.com/playlist?list=PL5B692fm6--tI-ijknnVZWbXU2H4JpSYe)
* [DeepBench](https://github.com/baidu-research/DeepBench).
* [Deep Fake source code](https://github.com/deepfakes/faceswap/).

#### Deep Learning Tools

* [Tensorflow plaground](http://playground.tensorflow.org).