anchor.diff:   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!
  +== Getting practical: make it run!
   
   Once you get a suitable system, it's time to choose the software and to get it running.
   There is certainly no _best_ language for Deep Learning, and I will not pretend to recommend any.
  
positive.code: In short, I claim that getting intuition is how it works.
  Intuition is *_FUNDAMENTAL_* to acquire knowledge.
  
  === Grabbing intuition about Deep Learning
  
  Hopefully, some people decided to created resources about Deep Learning that follow this philosophy.
  These resources are meant to bring Deep Learning to anyone with minimal skills in informatics and enough curiosity.
  
negative.code: The role of intuition
  --
  
  == The role of Intuition
  
  I started reading and experimenting about *Deep Learning* not so long ago and I found a great discrepancy in the affordability of resources available to _naïve_ learners.
  I've met a similar situation in image processing, where explanations about how an algorithm works and what it does is too often limited to inapprehensible equations.
  