anchor.diff:  
   But, playing with *Neural Networks* is not exactly trivial, not if you don't want to get bored very quickly. +
   Why that? +
  -Because these babies are hungry of computational power.
  +Because they're hungry for computational power.
   
   Well, they are not exactly monsters that will require thousands to be invested in your computer, they can run on pretty any system available these days.
   But they can be so *slooowwwwww...*
  
positive.code: 
  === Grab some intuition about Deep Learning
  
  Hopefully, some people decided that a field growing so large as Deep Learning. A field that already have a tremendous impact on our life.
  Such a field should not be reserved to the few able to build intuition out of that:
  [.text-center]
  .Some probably very interesting formula from an article published in link::http://www.scirp.org/journal/APM/[Advances in Pure Mathematics].
  [link=DOI:10.4236/apm.2013.39A1002]
  image::/images/Learning-Deep-Learning/MNN_formula.jpg[align=center, width=400]
  
  So these people created resources that are meant to bring Deep Learning to anyone with minimal skills in informatics and enough curiosity.
  Resources that help to build intuition out of practical examples, and allow anyone to learn in a self-paced schedule.
  
  There are two resources that exactly fit this description, and that I would like to share because of the outstanding quality of their content:
  
   * https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd[DeepLearning without a PhD], a 3 hours speech by https://plus.google.com/+MartinGorner[Martin Görner] (Google) that first demonstrate how to build a minimum Neural Network working with the famous MNIST dataset.
   Then improving it to a *Convolutional Neural Network (CNN)* that achieves up to 99.5% accuracy (top 10 of best results for this dataset).
   Both the videos (short and long versions), the code, the slides and an interactive codelab of this excellent demonstration are available.
   Plus a bonus demo of some *Recurrent Neural Network (RNN)* able to generate random text following the style of Shakespeare, or looking very much like the Python code of Tensorflow itself.
  
   * http://course.fast.ai/index.html[Practical Deep Learning For Coders], a 7 weeks formation by https://www.usfca.edu/data-institute/about-us/researchers[Jeremy Howard] (fast.ai, Data Institute at the University of San Francisco), freely available as a MOOC.
   The whole course is covering *CNN*, with some pre-trained models, *RNN* and *Natural Language Processing(NLP)*.
   Each week corresponds to a ~2H video, plus a Jupyter notebook and some assignments.
   Usually, students are encouraged to enter Kaggle competitions and play around with all resources.
   The website also has a nice and responsive forum with a growing community.
   Part 2 of this course should be released in the next months, and I am sure it will be as amazing as Part 1.
  
  The common aim of these two resources is to get to the practice as soon as possible.
  Only then dissect who does what, why and how.
  The fastest way to build intuition is to observe the thing running (or literally stare at it), play with it and think about it.
  
  
negative.code: === Getting practical: make it run!
  
  Once you get a suitable system, it's time to get the software installed.
  There is certainly no _best_ language for Deep Learning, and I will not pretend to recommend any.
  On my side I use *Python*, and obviously the resources I am talking about are using Python too.
  
  Many Python libraries are available for Deep Learning, and the choice might be difficult here also.
  I think https://keras.io/[Keras] is a good pick, as it is unifying some popular frameworks so you can jump from one to another very easily.
  