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# Resources for Machine Learning
## Tools
## In this Repositoryy
* [Tensorflow](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/TensorFlow).
* [Caffe](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Caffe).
* [Talks](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Talks).
* [Notebooks](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Notebooks).
* [Docker images](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Docker_Images).
* [Numpy](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Numpy).
* [Tensorflow](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/TensorFlow): examples in TF.
* [Caffe](https://github.com/bt3gl/Resources-Machine_Learning/tree/master/Caffe): examples in Caffe.
* [DeepArt](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 snippetes in Numpy.
----
## Learning
### Courses
### 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
### Deep Learning
* [A Chart of Neural Networks](http://www.asimovinstitute.org/neural-network-zoo/).