Neural Networks and Deep Learning

Neural Networks and Deep Learning

“Math Processing Error”
The page is attempting to use MathJax to render math symbols.
… you have caught the web page Ajax javascript code in an incomplete state.


  • Very basic programming skills (i.e. ability to work with dictionaries and for loops)
  • basic machine learning (how do we represent a dataset as a matrix, etc.).
  • basic linear algebra (matrix multiplications, vector operations etc.).

Python has a useful library “Numpy” that makes math operations very easy.
deep learning frameworks:

  • Tensorflow
  • Keras
  • PaddlePaddle,
  • CNTK
  • Caffe, …

Geoffrey Hinton interview
27:42 unsupervised learning: how to do it?


The code is more or less a black box

code_is_black_boxState-of-the-Art AI: Building Tomorrow’s Intelligent Systems
Peter Norvig, Director of Research, Google
EmTech Digital 2016. May 23, 2016

01:51 the code is more or less a black box, but you can look more carefully at it and figure out what is going on there with some degree

Evolving Robots Learn To Lie (2007)

Evolutionary Conditions for the Emergence of Communication in Robots
Current Biology 17, 514–519, March 20, 2007

Information transfer plays a central role in the biology of most organisms, particularly social species.
Although the neurophysiological processes by which signals are produced, conducted, perceived, and interpreted are well understood, the conditions conducive to the evolution of communication and the paths by which reliable systems of communication become established remain largely unknown.
This is a particularly challenging problem because efficient communication requires tight coevolution between the signal emitted and the response elicited.

We conducted repeated trials of experimental evolution with robots that could produce visual signals to provide information on food location. We found that communication readily evolves when colonies consist of genetically similar individuals and when selection acts at the colony level.

We identified several distinct communication systems that differed in their efficiency.
Once a given system of communication was well established, it constrained the evolution of more efficient communication systems. Under individual selection, the ability to produce visual signals resulted in the evolution of deceptive communication strategies in colonies of unrelated robots and a concomitant decrease in colony performance.
This study generates predictions about the evolutionary conditions conducive to the emergence of communication and provides guidelines for designing artificial evolutionary systems displaying spontaneous communication.


In this case, the ability to signal resulted in a deceptive signaling strategy associated with a significant decrease in colony performance compared to the situation where robots could not emit blue light. [p. 516]

Emission of light far from the food would then have evolved as a deceptive strategy for decreasing competition near the food. Consistent with this view, the tendency of robots to be attracted by blue light significantly decreased during the last 200 generations [p. 517]

evolution of cooperative communication

This study demonstrates that sophisticated forms of communication including cooperative communication and deceptive signaling can evolve in groups of robots with simple neural networks.
Importantly, our results show that once a given system of communication has evolved, it may constrain the evolution of more efficient communication systems because it would require going through a stage where communication between signalers and receivers is perturbed. This finding supports the idea of the possible arbitrariness and imperfection of communication systems, which can be maintained despite their suboptimal nature. [p. 517]

Laboratory of Intelligent Systems > Evolution of Communication
The aim of this project is to address questions on the emergence and evolution of communication in groups of social organisms by using evolutionary robotics to build societies of autonomous robots that evolve a communication system to solve a particular survival task collectively.

Laboratory of Intelligent Systems
Head of Lab: Prof. Dario Floreano