Deep Learning AMI for Ubuntu v1.3_Apr2017 Now Supports Caffe2

Deep Learning AMI on Amazon Web Services quickly added Caffe2 along with TensorFlow & others

  • We are excited to announce that the AWS Deep Learning AMI for Ubuntu now supports the newly launched Caffe2 project led by Facebook.
  • The Deep Learning AMI v1.3_Apr2017 for Ubuntu provides a stable, secure, and high-performance execution environment for deep learning applications running on Amazon EC2.
  • The AWS Deep Learning AMI (available for Amazon Linux and Ubuntu) and the AWS Deep Learning CloudFormation Template let you quickly deploy and run any of the major deep learning frameworks at any scale.
  • The AWS Deep Learning AMI is provided and supported by Amazon Web Services, for use on Amazon EC2.
  • There is no additional charge for the AWS Deep Learning AMI – you only pay for the AWS resources needed to store and run your applications.

We are excited to announce that the AWS Deep Learning AMI for Ubuntu now supports the newly launched Caffe2 project led by Facebook. AWS is the best and most open place for developers to run deep learning, and the addition of Caffe2 adds yet another choice. To learn more about Caffe2, check out the the Caffe2 developer site or the GitHub repository.
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Twilight’s Kristen Stewart co-authored a paper on artificial intelligence

Kristen Stewart co-wrote an academic paper about artificial intelligence

  • Kristen Stewart â the actress best known for “Twilight” â has co-written a paper on machine learning.
  • Kristen Stewart – the actress best known…
  • According to the paper, the project was based on an impressionistic painting of Stewart’s, which shows a man waking up.
  • The response to the paper has been positive from the academic community, if slightly bemused.
  • The paper, first spotted by Quartz , is co-bylined with Adobe research engineer Bhautik J. Joshi and producer David Shapiro.

The actress and director outlined the use of neural style transfer in her film ‘Come Swim’.
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Meet ‘Peeqo,’ the DIY robot that communicates via GIF

Meet 'Peeqo,' the DIY robot that communicates via GIF

  • Better still, its developer wrote a Chrome extension that uses the device to keep him in check when he should be working instead of playing on social media.
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  • Peeqo uses voice recognition from Google Speech API and API.ai to listen in on your queries and provide the appropriate GIF-based response.
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Are You Ready for Robot Colleagues?

Ready?

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  • The article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management.
  • Schmitt comes at the questions not as a computer scientist but as a marketing expert.
  • What’s happening the week at the intersection of management and technology.

In the future workplace, humans may supplement the skills of machines — and not the other way around.

Continue reading “Are You Ready for Robot Colleagues?”

Are You Ready for Robot Colleagues?

Ready?

  • To enjoy more articles like this one sign in , or create an account .
  • sign up for a free account : comment on articles and get access to many more articles.
  • What’s happening the week at the intersection of management and technology.
  • The article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management.
  • Schmitt comes at the questions not as a computer scientist but as a marketing expert.

In the future workplace, humans may supplement the skills of machines — and not the other way around.

Continue reading “Are You Ready for Robot Colleagues?”

srez/README.md at master · david-gpu/srez · GitHub

This is CSI-level: facial reconstruction via machine learning DCGA networks
 HT @diogomonica

  • The generator network relies on ResNet modules as we’ve found them to train substantially faster than more old-fashioned architectures.
  • ‘s an random, non cherry-picked, example of what this network can do.
  • The adversarial term of the loss function ensures the generator produces plausible faces, while the L1 term ensures that those faces resemble the low-res input data.
  • In addition to that the loss function of the generator has a term that measures the L1 difference between the 16×16 input and downscaled version of the image produced by the generator.
  • Extract all images to a subfolder named dataset .

srez – Image super-resolution through deep learning
Continue reading “srez/README.md at master · david-gpu/srez · GitHub”

GitHub

Code for super-resolution of faces, model is DCGAN, implementation TensorFlow:

  • The generator network relies on ResNet modules as we’ve found them to train substantially faster than more old-fashioned architectures.
  • The resulting 64×64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
  • We have found that this L1 term greatly accelerates the convergence of the network during the first batches and also appears to prevent the generator from getting stuck in a poor local solution.
  • ‘s an random, non cherry-picked, example of what this network can do.
  • Download zip file titled Align&Cropped Images and extract all images to a subfolder named dataset .

Read the full article, click here.


@Reza_Zadeh: “Code for super-resolution of faces, model is DCGAN, implementation TensorFlow:”


srez – Image super-resolution through deep learning


GitHub