# 🤖 [Scrath Space]  Machine Learning & Deep Learning projects and resources


* [ML Notebooks](https://github.com/bt3gl-labs/Scratch-Space-Machine-Learning-Projects-and-Research/tree/master/ml_notebooks): jupyter notebooks with ML examples
* [Tensorflow](https://github.com/bt3gl-labs/Scratch-Space-Machine-Learning-Projects-and-Research/tree/master/tensorflow_examples): learning examples
* [Caffe](https://github.com/bt3gl-labs/Scratch-Space-Machine-Learning-Projects-and-Research/tree/master/caffe): A example using Caffe library (docker container)
* [DeepArt](https://github.com/bt3gl-labs/Scratch-Space-Machine-Learning-Projects-and-Research/tree/master/deep_art): deep learning generated art
* [Numpy](https://github.com/bt3gl-labs/Scratch-Space-Machine-Learning-Projects-and-Research/tree/master/numpy_examples): my code and examples using Numpy
* [Data Engineering](https://github.com/bt3gl-labs/Scratch-Space-Machine-Learning-Projects-and-Research/tree/master/data-engineering): resources for deployment



---------

## Learning Resources


* [Energy-based Approaches to Representation Learning - Yann LeCun](https://www.youtube.com/watch?v=m17B-cXcZFI&amp=&t=524s).
* [Stanford's Machine Learning Course](http://cs229.stanford.edu/).
* [Google's Developer Machine Learning Course](https://developers.google.com/machine-learning).
* [Deep Learning Lectures by Lex Fridman](https://www.youtube.com/watch?v=O5xeyoRL95U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf).
* [Andrew Ng's deeplearning.ai](https://www.deeplearning.ai/deep-learning-specialization/)
* [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 by Ian Goodfellow and Yoshua Bengio and Aaron Courville](http://www.deeplearningbook.org/).
* [Tensorflow plaground](http://playground.tensorflow.org).
* [Google's Tensorflow courses](https://www.tensorflow.org/).
* [MIT Deep Learning Basics](https://medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0).