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  • Hi, internet.

  • This is Andres Rodriguez.

  • I am a machine and deep learning solutions architect.

  • Deep learning is a branch of machine learning

  • that attempts to model various levels of abstractions

  • and data.

  • It is popular for vision, speech, text, and control

  • tasks, among others.

  • There are various frameworks or tools to train and deploy

  • deep networks, such as Caffe, TensorFlow, Theano, Torch,

  • and the list continues to grow every year.

  • Intel is actively working with the deep learning community

  • to optimize many of these frameworks,

  • to maximize CPU utilization, and significantly

  • improve computational performance on Intel's

  • architectures.

  • A series of deep learning videos will

  • be available on the Intel Developer

  • Zone, where we will discuss some of the frameworks

  • and how to set them up to efficiently train and deploy

  • deep networks on Intel's architectures.

  • We will also discuss what Intel is

  • doing to optimize these frameworks,

  • both for single node and for multinode distributed training.

  • Some examples of deep learning applications

  • that can benefit from Intel's commitment

  • to reducing the time to train are image classification.

  • In this example, the input is an image,

  • and the output is the class label of the image.

  • Object detection.

  • The input is an image, and the outputs

  • are the bounding boxes of the objects in the image

  • and their respective labels.

  • Image segmentation.

  • The input is an image, and the output

  • is a semantically segmented image

  • where each pixel produces a label

  • corresponding to some object.

  • Natural language object retrieval.

  • The input to the network is a query

  • in natural language, where the answer is

  • an object in the image.

  • The network retrieves the object from the image.

  • Visual and text question answering.

  • The input is a query in natural language about the image,

  • and the output is the answer in natural language.

  • Visuomotor control.

  • The input is an image, and the output

  • is a motor function performed by a robot.

  • Speech recognition.

  • The same network configuration is

  • used to train an English and a Mandarin speech

  • recognition system.

  • And natural language understanding.

  • In this example, the input is a short story followed

  • by a question about the story.

  • The output is the answer.

  • There are many other examples, as deep learning is quickly

  • expanding to various areas.

  • At Intel, we're committed to enable industries and research

  • institutions to effectively and efficiently use

  • deep learning for their applications.

  • To learn more about this topic and others

  • related to machine learning, please follow the links

  • in the description below.

  • Thanks for watching.

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B1 deep learning image input learning natural language output

A Brief Overview of Deep Learning Using Intel® Architecture

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    alex posted on 2017/02/11
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