anchor.diff:  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!
  +=== Getting practical: get a computer
   
   But, playing with *Neural Networks* is not exactly trivial, not if you don't want to get bored very quickly. +
   Why that? +
  
positive.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.
  
  Once you get a suitable system, I advise to go for Python (did I said it? I *love* Python) and follow instructions to any of the Deep Learning frameworks installed.
  Keras is the one I would advise.
  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.
  
negative.code:  * 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)*.
  