Fast Style Transfer in TensorFlow

Add famous painting styles to any photo in a fraction of a second! You can even style videos!

It takes 100ms on a 2015 Titan X to style the MIT Stata Center (1024×680) like Udnie, by Francis Picabia.

Our implementation is based off of a combination of Gatys’ A Neural Algorithm of Artistic Style, Johnson’s Perceptual Losses for Real-Time Style Transfer and Super-Resolution, and Ulyanov’s Instance Normalization.

Python is the Growing Platform for Applied Machine Learning

You should pick the right tool for the job.

The specific predictive modeling problem that you are working on should dictate the specific programming language, libraries and even machine learning algorithms to use.

But, what if you are just getting started and looking for a platform to learn and practice machine learning?

In this post, you will discover that Python is the growing platform for applied machine learning, likely to outpace and topple R in terms of adoption and perhaps capability.

After reading this post you will know:

  • That search volume for Python machine learning is growing fast and has already outpaced R.
  • That the percentage of Python machine learning jobs is growing and has already outpaced R.
  • That Python is used by nearly 50% of polled practitioners and growing.

Let’s get started.

I don’t understand Python’s Asyncio

asyncio is supposed to implement asynchronous IO with the help of coroutines. Originally implemented as a library around the yield and yield from expressions it’s now a much more complex beast as the language evolved at the same time. So here is the current set of things that you need to know exist:

  • event loops
  • event loop policies
  • awaitables
  • coroutine functions
  • old style coroutine functions
  • coroutines
  • coroutine wrappers
  • generators
  • futures
  • concurrent futures
  • tasks
  • handles
  • executors
  • transports
  • protocols

Using Rowhammer bitflips to root Android phones is now a thing

Researchers have devised an attack that gains unfettered “root” access to a large number of Android phones, exploiting a relatively new type of bug that allows adversaries to manipulate data stored in memory chips.

The breakthrough has the potential to make millions of Android phones vulnerable, at least until a security fix is available, to a new form of attack that seizes control of core parts of the operating system and neuters key security defenses. Equally important, it demonstrates that the new class of exploit, dubbed Rowhammer, can have malicious and far-reaching effects on a much wider number of devices than was previously known, including those running ARM chips.

Previously, some experts believed Rowhammer attacks that altered specific pieces of security-sensitive data weren’t reliable enough to pose a viable threat because exploits depended on chance hardware faults or advanced memory-management features that could be easily adapted to repel the attacks. But the new proof-of-concept attack developed by an international team of academic researchers is challenging those assumptions.

An app containing the researchers’ rooting exploit requires no user permissions and doesn’t rely on any vulnerability in Android to work. Instead, their attack exploits a hardware vulnerability, using a Rowhammer exploit that alters crucial bits of data in a way that completely roots name brand Android devices from LG, Motorola, Samsung, OnePlus, and possibly other manufacturers.