Deep learning

Software libraries

  • Deeplearning4j — An open-source deep-learning library written for Java/C++ with LSTMs and convolutional networks. It provides parallelization with Spark on CPUs and GPUs.
  • Gensim — A toolkit for natural language processing implemented in the Python programming language.
  • Keras — An open-source deep learning framework for the Python programming language.
  • Microsoft CNTK (Computational Network Toolkit) — Microsoft’s open-source deep-learning toolkit for Windows and Linux. It provides parallelization with CPUs and GPUs across multiple servers.
  • MXNet — An open source deep learning framework that allows you to define, train, and deploy deep neural networks.
  • OpenNN — An open source C++ library which implements deep neural networks and provides parallelization with CPUs.
  • PaddlePaddle — An open source C++ /CUDA library with Python API for scalable deep learning platform with CPUs and GPUs, originally developed by Baidu.
  • TensorFlow — Google’s open source machine learning library in C++ and Python with APIs for both. It provides parallelization with CPUs and GPUs.
  • Theano — An open source machine learning library for Python supported by the University of Montreal and Yoshua Bengio’s team.
  • Torch — An open source software library for machine learning based on the Lua programming language and used by Facebook.
  • Caffe – Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
  • DIANNE – A modular open-source deep learning framework in Java / OSGi developed at Ghent University, Belgium. It provides parallelization with CPUs and GPUs across multiple servers.

Go engine

Mastering the game of Go with deep neural networks and tree search

David Silver, Aja Huang1, Chris J. Maddison, Arthur Guez, Laurent Sifre1, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe,
John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach1, Koray Kavukcuoglu,
Thore Graepel1, Demis Hassabis

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm,our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
Continue reading “Go engine”

Real Programmers write in FORTRAN

Real Programmers write in FORTRAN

Maybe they do now,
in this decadent era of
Lite beer, hand calculators, and “user-friendly” software
but back in the Good Old Days,
when the term “software” sounded funny
and Real Computers were made out of drums and vacuum tubes,
Real Programmers wrote in machine code.
Not FORTRAN. Not RATFOR. Not, even, assembly language.
Machine Code.
Raw, unadorned, inscrutable hexadecimal numbers.

Data explorer


In case you do not live in New York City or you did not attend Data Gotham, do not worry because nearly all the videos and talks are posted on the Data Gotham 2013 Youtube page.

Logan Symposium: Google Public Data Explorer from Berkeley Graduate School of Journalism on

4th Annual Logan Symposium on Investigative Reporting


Uploaded on Jun 2, 2010

Complete video at:…

Using Google’s new Public Data Explorer tool, Ola Rosling demonstrates the effectiveness of visualizing datasets. Looking toward the next political election, Rosling hopes voters will use the tool to answer questions like: How was the money spent? Where are the biggest problems?


Ola Rosling of Google Public Data gives a presentation titled, “Google Public Data Explorer” at the Berkeley Graduate School of Journalism. This program was recorded on April 18, 2010.

Ola Rosling co-founded the Gapminder Foundation and led the development of Trendalyzer, a software that converts time series statistics into animated, interactive and comprehensible graphics. The aim of his work is to promote a fact-based world view through increased use and understanding of freely accessible public data.

In March 2007, Google acquired the Trendalyzer software, where Rosling and his team are now scaling up their tools and making them freely available for any individual or organization to use for analyzing and visualizing data.