anchor.diff:   * 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 Shakespear, or looking very much like the Python code of Tensorflow itself.
  + 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)*.
  
positive.code: 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.
  
  === Getting practical: make it run!
  
  But, playing with *Neural Networks* is not exactly trivial, not if you don't want to get bored very quickly. +
  Why that? +
  
negative.code:  Almost any mid-range GPU will be able to get you started if needed, but there are definitely important criterions to be taken into account if you want to get serious job done.
    http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning/[Which GPU to get for Deep Learning] is very nice article on this subject by Tim Dettmers. The author is very responsive, so do not hesitate to ask for more precisions in the comments.
  
  === 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, but if you're starting from scratch (or if you know the bases already) I would recommend *Python*.
  
  It is clearly getting more and more popular in this field (and machine learning in general), and the community is growing fast and very dynamic.
  
  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.
  https://www.tensorflow.org/[TensorFlow] and http://deeplearning.net/software/theano/#[Theano] are fully supported, and https://docs.microsoft.com/en-us/cognitive-toolkit/[CNTK] and http://mxnet.io/[MXNET] are coming right now (beta).
  Reviews of the different frameworks are available online (for example https://indico.io/blog/python-deep-learning-frameworks-reviewed/[here]).
  
  
  First you'll have plenty examples if you have a look to fast.ai.
  Second it allows to use TensorFlow or Theano, which are two different frameworks with their own advantages.
  Third it is growing popular and will soon allow to use CNTK, and MXNET which are two additional frameworks.
  