About Carlos Perez
Carlos Perez is a software developer presently writing a book on "Design Patterns for Deep Learning". This is where he sources his ideas for his blog posts.
If you are interested in finding out how Deep Learning can help you in your work, please feel to leave your email at: www.intuitionmachine.com or join the discussion in Facebook: www.facebook.com/groups/deeplearningpatterns/.
Carlos Perez Posts (12)

Artificial Intuition – A Breakthrough Cognitive Paradigm  18 Jul 2017
This article is just a reflection of my current understanding of the language of Deep Learning Meta MetaModel. That’s definitely a mouth full, so to make life simpler for everyone, I just call this the Deep Learning Canonical Patterns.

The Strange Loop in Deep Learning  11 Jul 2017
This ‘strange loop’ is in fact is the fundamental reason for what Yann LeCun describes as “the coolest idea in machine learning in the last twenty years.”

Taxonomy of Methods for Deep Meta Learning  22 Jun 2017
This post discusses a variety of contemporary Deep Meta Learning methods, in which metadata is manipulated to generate simulated architectures. Current metalearning capabilities involve either support for search for architectures or networks inside networks.

The Two Phases of Gradient Descent in Deep Learning  12 May 2017
In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!

One Deep Learning Virtual Machine to Rule Them All  28 Apr 2017
The frontend code of programming languages only needs to parse and translate source code to an intermediate representation (IR). Deep Learning frameworks will eventually need their own “IR.”

Greed, Fear, Game Theory and Deep Learning  03 Mar 2017
The most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. The core problem of a multiagent approach is how to control its behavior.

Deep Learning, Artificial Intuition and the Quest for AGI  20 Feb 2017
Deep Learning systems exhibit behavior that appears biological despite not being based on biological material. It so happens that humanity has luckily stumbled upon Artificial Intuition in the form of Deep Learning.

Turbo Charge Agile Processes with Deep Learning  07 Feb 2017
The key to leveraging Deep Learning, or more broadly AI, in the workplace is to understand where it fits within an agile development environment.

Deep Learning Can be Applied to Natural Language Processing  16 Jan 2017
This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.

Game Theory Reveals the Future of Deep Learning  29 Dec 2016
This post covers the emergence of Game Theoretic concepts in the design of newer deep learning architectures. Deep learning systems need to be adaptive to imperfect knowledge and coordinating systems, 2 areas with which game theory can help.