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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
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.
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A Brief Overview of Deep Learning Using Intel® Architecture

167 Folder Collection
alex published on February 11, 2017
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