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: === 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.
  
negative.code: 
  === Grab some 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.
  They aim to help building 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 demonstrate how to build a Neural Network working with the famous MNIST dataset.
   Several architectures are tested, demonstrating how to tune the network up to a *Convolutional Neural Network (CNN)* that achieves up to 99.5% accuracy (top 10 of best results for this dataset).
   Final part is a demo of some *Recurrent Neural Network (RNN)* able to generate random text following the style of Shakespeare's play, or the syntax of the Python code of Tensorflow itself.
   Both the videos (short and long versions), the code, the slides and an interactive codelab of this excellent demonstration are available.
  
   * 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*, demonstrating the use of pre-trained models, *RNN* and *Natural Language Processing(NLP)*.
   Each week corresponds to a ~2H video, plus a Jupyter notebook and assignments.
   Usually, students are encouraged to enter Kaggle competitions and play around with all resources available.
   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 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.
  
  