Intel Democratizes Deep Learning Application Development with Launch of Movidius Neural Compute Stick

Introducing the world’s first USB-based #deeplearning inference kit:  #Intel

  • Today, Intel launched the Movidius™ Neural Compute Stick, the world’s first USB-based deep learning inference kit and self-contained artificial intelligence (AI) accelerator that delivers dedicated deep neural network processing capabilities to a wide range of host devices at the edge.
  • Designed for product developers, researchers and makers, the Movidius Neural Compute Stick aims to reduce barriers to developing, tuning and deploying AI applications by delivering dedicated high-performance deep-neural network processing in a small form factor.
  • More: Movidius Press Kit | Movidius Neural Compute Stick Product Brief | Intel at CVPR Fact Sheet

    As more developers adopt advanced machine learning approaches to build innovative applications and solutions, Intel is committed to providing the most comprehensive set of development tools and resources to ensure developers are retooling for an AI-centric digital economy.

  • Whether it is training artificial neural networks on the Intel® Nervana™ cloud, optimizing for emerging workloads such as artificial intelligence, virtual and augmented reality, and automated driving with Intel® Xeon® Scalable processors, or taking AI to the edge with Movidius vision processing unit (VPU) technology, Intel offers a comprehensive AI portfolio of tools, training and deployment options for the next generation of AI-powered products and services.
  • “The Myriad 2 VPU housed inside the Movidius Neural Compute Stick provides powerful, yet efficient performance – more than 100 gigaflops of performance within a 1W power envelope – to run real-time deep neural networks directly from the device,” said Remi El-Ouazzane, vice president and general manager of Movidius, an Intel company.

Today, Intel launched the Movidius™ Neural Compute Stick, the world’s first USB-based deep learning inference kit and self-contained artificial intelligence (AI) accelerator that delivers dedicated deep neural network processing capabilities to a wide range of host devices at the edge. Designed for product developers, researchers and makers, the Movidius Neural Compute Stick aims to reduce barriers to developing, tuning and deploying AI applications by delivering dedicated high-performance deep-neural network processing in a small form factor.
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Google’s DeepMind turns to StarCraft II after conquering Go

Google's DeepMind turns to StarCraft II after conquering Go | ZDNet  #ai

  • Blizzard and DeepMind have created an open test environment within the StarCraft II game for artificial intelligence researchers to use worldwide.
  • Google’s DeepMind has announced that it will be making use of game development studio Blizzard’s StarCraft II game as a testing platform for artificial intelligence (AI) and machine-learning research, opening the environment worldwide.
  • StarCraft II is closer to a real-world environment than any other game it has used for testing so far, DeepMind said, as it is played in real-time.
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  • “Games are the perfect environment in which to do this, allowing us to develop and test smarter, more flexible AI algorithms quickly and efficiently, and also providing instant feedback on how we’re doing through scores.”

Blizzard and DeepMind have created an open test environment within the StarCraft II game for artificial intelligence researchers to use worldwide.
Continue reading “Google’s DeepMind turns to StarCraft II after conquering Go”

An Introduction to Implementing Neural Networks using TensorFlow

An Introduction to Implementing Neural Networks using TensorFlow   #python

  • General way to solve problems with Neural Networks
  • After defining our neural network architecture, let’s initialize all the variables
  • We need to define cost of our neural network
  • We define a neural network with 3 layers;Â input, hidden and output.
  • It’s easy to classify TensorFlow as a neural network library, but it’s not just that.

An introduction to implement neural networks using TensorFlow. It covers applications of neural networks, introduction to Tensorflow & a practice problem
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Deep Learning Libraries by Language

#DeepLearning Libraries by Language

  • darch package can be used for generating neural networks with many layers (deep architectures).
  • Convnet.js is a Javascript library for training Deep Learning models (mainly Neural Networks) entirely in a browser.
  • DeepLearning is deep learning library, developed with C++ and python.
  • Pylearn2 is a library that wraps a lot of models and training algorithms such as Stochastic Gradient Descent that are commonly used in Deep Learning.
  • Intel® Deep Learning Framework provides a unified framework for Intel® platforms accelerating Deep Convolutional Neural Networks.

Read the full article, click here.


@analyticbridge: “#DeepLearning Libraries by Language”


Source for picture: click here
Python

Theano is a python library for defining and evaluating mathematical expressions with numerical arrays.

Keras is a mi…


Deep Learning Libraries by Language

[1603.02518] A New Method to Visualize Deep Neural Networks

A New Method to Visualize Deep Neural Networks  #visualization #deeplearning #MachineLearning

  • Abstract: We present a method for visualising the response of a deep neural network to a specific input.
  • Title: A New Method to Visualize Deep Neural Networks
  • The method overcomes several shortcomings of previous methods and provides great additional insight into the decision making process of convolutional networks, which is important both to improve models and to accelerate the adoption of such methods in e.g. medicine.
  • For image data for instance our method will highlight areas that provide evidence in favor of, and against choosing a certain class.
  • In experiments on ImageNet data, we illustrate how the method works and can be applied in different ways to understand deep neural nets.

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@quantombone: “A New Method to Visualize Deep Neural Networks #visualization #deeplearning #MachineLearning”



[1603.02518] A New Method to Visualize Deep Neural Networks

Residual neural networks are an exciting area of deep learning research — Init.ai Decoded

Residual neural networks are an exciting area of #deeplearning research. 1000 layers! #AI

  • The paper Deep Residual Networks with Exponential Linear Unit , by Shah et al., combines exponential linear units, an alternative to rectified linear units, with ResNets to show improved performance, even without batch normalization.
  • ResNets will be important to enable complex models of the world.
  • ResNets tweak the mathematical formula for a deep neural network.
  • The paper enables practical training of neural networks with thousands of layers.
  • I am highlighting several recent papers that show the potential of residual neural networks.

Read the full article, click here.


@StartupYou: “Residual neural networks are an exciting area of #deeplearning research. 1000 layers! #AI”


The identity function is simply id(x) = x; given an input x it returns the same value x as output.


Residual neural networks are an exciting area of deep learning research — Init.ai Decoded