Syrias children learn to code with the Raspberry Pi

Three years ago, when I was looking for an example of social unrest to highlight the use of social media as a communication tool for protestors in my book, I chose the then new uprising in Syria. Im horrified the conflict still continues. However, I just came across a surprisingly good piece of news from that awful conflict; the use of the Raspberry Pi to teach Syrian refugees in Lebanon to code. Read the full article in the Guardian to learn more.

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The Untold Story of Women Who Code

Computer programming is typically seen as being an activity that is done by young men of a certain type. Most TV shows (e.g., Silicon Valley) and movies conform to this stereotype. However, its far from the truth; Lady Ada Lovelace is often referred to as the "first computer programmer" and Grace Hopper famously wrote the first compiler and coined the term "computer bug." A YouTube video, shared with my by my colleague Mark Wilson, featuring Megan Smith, the former Google executive recently appointed by President Obama as the Chief Technology Officer of the United States, further debunks this myth.

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20 Resources for Teaching Kids How to Program Code

Educators now admit that the past decades of ITC teaching were flawed. Teaching kids to use MS Word or PowerPoint is not empowering them to join the IT revolution. Current thinking is that everyone should know how to code (at least the fundamentals). This will help everyone understand that computer programs arent something magical, that only a select few can create, but a tool anyone can use. To this end more and more ways of teaching kids to code are being created. This article lists "20 Resources for Teaching Kids How to Program & Code".



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DeepDream a code example for visualizing Neural Networks

Posted by Alexander Mordvintsev, Software Engineer, Christopher Olah, Software Engineering Intern and Mike Tyka, Software Engineer

Two weeks ago we blogged about a visualization tool designed to help us understand how neural networks work and what each layer has learned. In addition to gaining some insight on how these networks carry out classification tasks, we found that this process also generated some beautiful art.

Top: Input image. Bottom: output image made using a network trained on places by MIT Computer Science and AI Laboratory.

We have seen a lot of interest and received some great questions, from programmers and artists alike, about the details of how these visualizations are made. We have decided to open source the code we used to generate these images in an IPython notebook, so now you can make neural network inspired images yourself!

The code is based on Caffe and uses available open source packages, and is designed to have as few dependencies as possible. To get started, you will need the following (full details in the notebook):

• NumPy, SciPy, PIL, IPython, or a scientific python distribution such as Anaconda or Canopy.
• Caffe deep learning framework (Installation instructions)

Once you’re set up, you can supply an image and choose which layers in the network to enhance, how many iterations to apply and how far to zoom in. Alternatively, different pre-trained networks can be plugged in.

Itll be interesting to see what imagery people are able to generate. If you post images to Google+, Facebook, or Twitter, be sure to tag them with #deepdream so other researchers can check them out too.

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What does the scrambling code do and function

 

 

Question: What does the scrambling code do and function?

 

Answer: Scrambling Code makes it possible for the UE to distinguish the  transmissions from different cells/NodeBs. Bonus if he knows there are 512 primary scrambling codes and that the are broken up to 64 groups of 8 codes each.

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