Merge pull request #4 from CodingTrain/readme/rnns

Added a few links and added (subjective) skill level requirements

Author: shiffman

README.md

@@ -1,6 +1,16 @@
 # Machine-Learning
 Examples and experiments around ML for upcoming Coding Train videos and ITP course.
 
+# Resource attributes
+
+Since resources across the internet vary in terms of their pre-requisites and general accessibility, it is useful to
+give attributes to them so that it is easy to understand where a resource fits into the wider machine learning scope. Below is a few suggested attributes (please extend):
+
+ - :rainbow: = creative
+ - :bowtie: = beginner
+ - :sweat_smile: = intermediate, some pre-requisites
+ - :godmode: = advanced, many pre-requisites
+
 # Table of Contents
 <!-- MarkdownTOC depth=4 -->
 - [Articles & Posts](#articles--posts)
@@ -14,53 +24,57 @@ Examples and experiments around ML for upcoming Coding Train videos and ITP cour
 
 <!-- /MarkdownTOC -->
 ## Articles & Posts
-  1. [A Return to Machine Learning](https://medium.com/@kcimc/a-return-to-machine-learning-2de3728558eb#.vlqnbo9yg)
-  1. [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
-  1. [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)
-  1. [Inside Libratus, the Poker AI That Out-Bluffed the Best Humans](https://www.wired.com/2017/02/libratus/?imm_mid=0ed017&cmp=em-data-na-na-newsltr_ai_20170206)
-  1. [Machine Learning in Javascript: Introduction](http://burakkanber.com/blog/machine-learning-in-other-languages-introduction/)
-  1. [Realtime control of sequence generation with character based Long Short Term Memory Recurrent Neural Networks](http://www.iggi.org.uk/assets/IGGI-2016-Memo-A.pdf)
-  1. [Why is machine learning 'hard'?](http://ai.stanford.edu/~zayd/why-is-machine-learning-hard.html)
+  1. [A Return to Machine Learning](https://medium.com/@kcimc/a-return-to-machine-learning-2de3728558eb#.vlqnbo9yg) :rainbow: :bowtie:
+  1. [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) :rainbow: :bowtie:
+  1. [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471) :bowtie:
+  1. [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/) :rainbow: 
+  1. [Inside Libratus, the Poker AI That Out-Bluffed the Best Humans](https://www.wired.com/2017/02/libratus/? imm_mid=0ed017&cmp=em-data-na-na-newsltr_ai_20170206) :bowtie:
+  1. [Machine Learning in Javascript: Introduction](http://burakkanber.com/blog/machine-learning-in-other-languages-introduction/) :bowtie:
+  1. [Realtime control of sequence generation with character based Long Short Term Memory Recurrent Neural Networks](http://www.iggi.org.uk/assets/IGGI-2016-Memo-A.pdf) :sweat_smile:
+  1. [Why is machine learning 'hard'?](http://ai.stanford.edu/~zayd/why-is-machine-learning-hard.html) :bowtie:
+  1. [Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) :sweat_smile:
 
 ## Courses
-  1. [The Neural Aesthetic @ SchoolOfMa, Summer 2016](http://ml4a.github.io/classes/neural-aesthetic/)
+  1. [The Neural Aesthetic @ SchoolOfMa, Summer 2016](http://ml4a.github.io/classes/neural-aesthetic/) :rainbow: :bowtie:
 
 ## Examples
-  1. [A Deep Q Reinforcement Learning Demo](http://projects.rajivshah.com/rldemo/)
-  1. [How to use Q Learning in Video Games Easily](https://github.com/llSourcell/q_learning_demo)
-  1. [K-nearest](https://twitter.com/MaximilianLloyd/status/814942799351185408)
-  1. [The Infinite Drum Machine](https://aiexperiments.withgoogle.com/drum-machine/view/)
-  1. [Visualizing the perceptron training algorithm](https://kwichmann.github.io/ml_sandbox/perceptron/)
+  1. [A Deep Q Reinforcement Learning Demo](http://projects.rajivshah.com/rldemo/) :bowtie: 
+  1. [How to use Q Learning in Video Games Easily](https://github.com/llSourcell/q_learning_demo) :rainbow: :bowtie:
+  1. [K-nearest](https://twitter.com/MaximilianLloyd/status/814942799351185408) :bowtie: 
+  1. [The Infinite Drum Machine](https://aiexperiments.withgoogle.com/drum-machine/view/) :rainbow: :bowtie:
+  1. [Visualizing the perceptron training algorithm](https://kwichmann.github.io/ml_sandbox/perceptron/) :rainbow: :bowtie:
 
 ## Projects
-  1. [Bidirectional LSTM for IMDB sentiment classification](https://transcranial.github.io/keras-js/#/imdb-bidirectional-lstm)
-  1. [Land Lines](https://medium.com/@zachlieberman/land-lines-e1f88c745847#.1157xmhw8)
-  1. [nnvis - Topological Visualisation of a Convolutional Neural Network](http://terencebroad.com/convnetvis/vis.html)
+  1. [Bidirectional LSTM for IMDB sentiment classification](https://transcranial.github.io/keras-js/#/imdb-bidirectional-lstm) :sweat_smile:
+  1. [Land Lines](https://medium.com/@zachlieberman/land-lines-e1f88c745847#.1157xmhw8) 
+  1. [nnvis - Topological Visualisation of a Convolutional Neural Network](http://terencebroad.com/convnetvis/vis.html) :rainbow: :bowtie:
+  1. [char-rnn A character level language model (a fancy text generator)](https://github.com/karpathy/char-rnn) :rainbow: :sweat_smile:
+  
 
 ## Videos
   * Reinforcement Learning
-    1. [Artificial Intelligence in Google's Dinosaur (English Sub)](https://www.youtube.com/watch?v=P7XHzqZjXQs)
-    1. [How to use Q Learning in Video Games Easily](https://www.youtube.com/watch?v=A5eihauRQvo&feature=youtu.be)
+    1. [Artificial Intelligence in Google's Dinosaur (English Sub)](https://www.youtube.com/watch?v=P7XHzqZjXQs) :bowtie:
+    1. [How to use Q Learning in Video Games Easily](https://www.youtube.com/watch?v=A5eihauRQvo&feature=youtu.be) :bowtie:
   * Evolutionary Algorithms
-    1. [Evolving Swimming Soft-Bodied Creatures](https://www.youtube.com/watch?v=4ZqdvYrZ3ro)
-    1. [Harnessing evolutionary creativity: evolving soft-bodied animats in simulated physical environments](https://www.youtube.com/watch?v=CXTZHHQ7ZiQ&feature=youtu.be)
-    1. [Reproduce image with genetic algorithm](https://www.youtube.com/watch?v=iV-hah6xs2A)
+    1. [Evolving Swimming Soft-Bodied Creatures](https://www.youtube.com/watch?v=4ZqdvYrZ3ro) :rainbow: :bowtie:
+    1. [Harnessing evolutionary creativity: evolving soft-bodied animats in simulated physical environments](https://www.youtube.com/watch?v=CXTZHHQ7ZiQ&feature=youtu.be) :rainbow: :bowtie:
+    1. [Reproduce image with genetic algorithm](https://www.youtube.com/watch?v=iV-hah6xs2A) :bowtie:
 
 ## Tools
-  1. [ConvNetJS - Javascript library for training Deep Learning models (Neural Networks) ](http://cs.stanford.edu/people/karpathy/convnetjs/)
-  1. [RecurrentJS - Deep Recurrent Neural Networks and LSTMs in Javascript](https://github.com/shiffman/recurrentjs)
-  1. [WORD2VEC](http://technobium.com/find-words-similarity-using-deeplearning4j-word2vec/)
+  1. [ConvNetJS - Javascript library for training Deep Learning models (Neural Networks) ](http://cs.stanford.edu/people/karpathy/convnetjs/) :sweat_smile: 
+  1. [RecurrentJS - Deep Recurrent Neural Networks and LSTMs in Javascript](https://github.com/shiffman/recurrentjs) :sweat_smile:
+  1. [WORD2VEC](http://technobium.com/find-words-similarity-using-deeplearning4j-word2vec/) :sweat_smile:
 
 ### TensorFlow
-  1. [Projector](http://projector.tensorflow.org/)
-  1. [Magenta](https://github.com/tensorflow/magenta)
+  1. [Projector](http://projector.tensorflow.org/) :sweat_smile:
+  1. [Magenta](https://github.com/tensorflow/magenta) :rainbow:
 
 ### Tensorflow posts
   1. [Big deep learning news: Google Tensorflow chooses Keras](http://www.fast.ai/2017/01/03/keras/)
   1. [Simple end-to-end TensorFlow examples](http://bcomposes.com/2015/11/26/simple-end-to-end-tensorflow-examples/)
 
 ### t-SNE
-  1. [t-SNE](https://lvdmaaten.github.io/tsne/)
-  1. [t-SNE](https://scienceai.github.io/tsne-js/)
+  1. [t-SNE](https://lvdmaaten.github.io/tsne/) :sweat_smile:
+  1. [t-SNE](https://scienceai.github.io/tsne-js/) :sweat_smile:
   1. [An illustrated introduction to the t-SNE algorithm](https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm)
-  1. [Visualizing Data Using t-SNE](https://www.youtube.com/watch?v=RJVL80Gg3lA&list=UUtXKDgv1AVoG88PLl8nGXmw)
+  1. [Visualizing Data Using t-SNE](https://www.youtube.com/watch?v=RJVL80Gg3lA&list=UUtXKDgv1AVoG88PLl8nGXmw) :rainbow: