# 🍓 My Resources for AI, ML, and DNN 🍓 ## Learning ## Getting the News * Feedly with [list of blogs to follow](https://raw.githubusercontent.com/bt3gl/Machine-Learning-Resources/master/ml_ai_feedly.opml). * Check [my blog](http://bt3gl.github.io/) :). * [Deep Learning weekly](http://www.deeplearningweekly.com/). ## Machine Learning in General * [Stanford's Machine Learning Course](http://cs229.stanford.edu/) * [A Chart of Neural Networks](http://www.asimovinstitute.org/neural-network-zoo/). ### Fun: * [Machine Learning for Artists](http://ml4a.github.io/index/). * [LossFunctions.tumblr](http://lossfunctions.tumblr.com/). * [CreativeAI](http://www.creativeai.net/). ## Deep Learning ### Reinforcement Learning * [UCL Course on RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) ### ConvNets * [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). ### Hardware * [NVIDIA Deep Learning Course](https://www.youtube.com/playlist?list=PL5B692fm6--tI-ijknnVZWbXU2H4JpSYe) ### Computer Vision * [Multiple View Geometry in CV](https://www.goodreads.com/book/show/18938711-multiple-view-geometry-in-computer-vision). ## Working ### Benchmarkers * [DeepBench](https://github.com/baidu-research/DeepBench).