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-----------------
Linux CPULinux GPUMac OS CPUWindows CPUAndroid
Build StatusBuild StatusBuild StatusBuild StatusBuild Status

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

If you'd like to contribute to TensorFlow, be sure to review the contribution guidelines.

We use GitHub issues for tracking requests and bugs, but please see Community for general questions and discussion.

Installation

See Download and Setup for instructions on how to install our release binaries or how to build from source.

People who are a little more adventurous can also try our nightly binaries:

Try your first TensorFlow program

$ python
>>>importtensorflowastf>>>hello=tf.constant('Hello, TensorFlow!')
>>>sess=tf.Session()
>>>sess.run(hello)
Hello, TensorFlow!
>>>a=tf.constant(10)
>>>b=tf.constant(32)
>>>sess.run(a+b)
42>>>

##For more information

The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.

About

Computation using data flow graphs for scalable machine learning

tensorflow.org

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-----------------

Linux CPULinux GPUMac OS CPUWindows CPUAndroid
Build StatusBuild StatusBuild StatusBuild StatusBuild Status
TensorFlow is an open source software library for numerical computation using
data flow graphs. Nodes in the graph represent mathematical operations, while
the graph edges represent the multidimensional data arrays (tensors) that flow
between them. This flexible architecture lets you deploy computation to one
or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers
working on the Google Brain team within Google's Machine Intelligence research
organization for the purposes of conducting machine learning and deep neural
networks research. The system is general enough to be applicable in a wide
variety of other domains, as well.
If you'd like to contribute to TensorFlow, be sure to review the contribution
guidelines.
We use GitHub issues for
tracking requests and bugs, but please see
Community for general questions
and discussion.
Installation
See Download and Setup for instructions on how to install our release binaries or how to build from source.
People who are a little more adventurous can also try our nightly binaries:
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>hello=tf.constant('Hello, TensorFlow!')
>>>sess=tf.Session()
>>>sess.run(hello)
Hello, TensorFlow!
>>>a=tf.constant(10)
>>>b=tf.constant(32)
>>>sess.run(a+b)
42>>>
##For more information
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.
About
Computation using data flow graphs for scalable machine learning
tensorflow.org
Resources
Readme
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Repository files navigation



-----------------

Linux CPULinux GPUMac OS CPUWindows CPUAndroid
Build StatusBuild StatusBuild StatusBuild StatusBuild Status
TensorFlow is an open source software library for numerical computation using
data flow graphs. Nodes in the graph represent mathematical operations, while
the graph edges represent the multidimensional data arrays (tensors) that flow
between them. This flexible architecture lets you deploy computation to one
or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers
working on the Google Brain team within Google's Machine Intelligence research
organization for the purposes of conducting machine learning and deep neural
networks research. The system is general enough to be applicable in a wide
variety of other domains, as well.
If you'd like to contribute to TensorFlow, be sure to review the contribution
guidelines.
We use GitHub issues for
tracking requests and bugs, but please see
Community for general questions
and discussion.
Installation
See Download and Setup for instructions on how to install our release binaries or how to build from source.
People who are a little more adventurous can also try our nightly binaries:
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>hello=tf.constant('Hello, TensorFlow!')
>>>sess=tf.Session()
>>>sess.run(hello)
Hello, TensorFlow!
>>>a=tf.constant(10)
>>>b=tf.constant(32)
>>>sess.run(a+b)
42>>>
##For more information
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.
About
Computation using data flow graphs for scalable machine learning
tensorflow.org
Resources
Readme
Apache-2.0 license
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Repository files navigation



-----------------

Linux CPULinux GPUMac OS CPUWindows CPUAndroid
Build StatusBuild StatusBuild StatusBuild StatusBuild Status
TensorFlow is an open source software library for numerical computation using
data flow graphs. Nodes in the graph represent mathematical operations, while
the graph edges represent the multidimensional data arrays (tensors) that flow
between them. This flexible architecture lets you deploy computation to one
or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers
working on the Google Brain team within Google's Machine Intelligence research
organization for the purposes of conducting machine learning and deep neural
networks research. The system is general enough to be applicable in a wide
variety of other domains, as well.
If you'd like to contribute to TensorFlow, be sure to review the contribution
guidelines.
We use GitHub issues for
tracking requests and bugs, but please see
Community for general questions
and discussion.
Installation
See Download and Setup for instructions on how to install our release binaries or how to build from source.
People who are a little more adventurous can also try our nightly binaries:
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>hello=tf.constant('Hello, TensorFlow!')
>>>sess=tf.Session()
>>>sess.run(hello)
Hello, TensorFlow!
>>>a=tf.constant(10)
>>>b=tf.constant(32)
>>>sess.run(a+b)
42>>>
##For more information
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.
About
Computation using data flow graphs for scalable machine learning
tensorflow.org
Resources
Readme
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-----------------

Linux CPULinux GPUMac OS CPUWindows CPUAndroid
Build StatusBuild StatusBuild StatusBuild StatusBuild Status
TensorFlow is an open source software library for numerical computation using
data flow graphs. Nodes in the graph represent mathematical operations, while
the graph edges represent the multidimensional data arrays (tensors) that flow
between them. This flexible architecture lets you deploy computation to one
or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers
working on the Google Brain team within Google's Machine Intelligence research
organization for the purposes of conducting machine learning and deep neural
networks research. The system is general enough to be applicable in a wide
variety of other domains, as well.
If you'd like to contribute to TensorFlow, be sure to review the contribution
guidelines.
We use GitHub issues for
tracking requests and bugs, but please see
Community for general questions
and discussion.
Installation
See Download and Setup for instructions on how to install our release binaries or how to build from source.
People who are a little more adventurous can also try our nightly binaries:
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>hello=tf.constant('Hello, TensorFlow!')
>>>sess=tf.Session()
>>>sess.run(hello)
Hello, TensorFlow!
>>>a=tf.constant(10)
>>>b=tf.constant(32)
>>>sess.run(a+b)
42>>>
##For more information
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.
About
Computation using data flow graphs for scalable machine learning
tensorflow.org
Resources
Readme
Apache-2.0 license
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Repository files navigation



-----------------

Linux CPULinux GPUMac OS CPUWindows CPUAndroid
Build StatusBuild StatusBuild StatusBuild StatusBuild Status
TensorFlow is an open source software library for numerical computation using
data flow graphs. Nodes in the graph represent mathematical operations, while
the graph edges represent the multidimensional data arrays (tensors) that flow
between them. This flexible architecture lets you deploy computation to one
or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers
working on the Google Brain team within Google's Machine Intelligence research
organization for the purposes of conducting machine learning and deep neural
networks research. The system is general enough to be applicable in a wide
variety of other domains, as well.
If you'd like to contribute to TensorFlow, be sure to review the contribution
guidelines.
We use GitHub issues for
tracking requests and bugs, but please see
Community for general questions
and discussion.
Installation
See Download and Setup for instructions on how to install our release binaries or how to build from source.
People who are a little more adventurous can also try our nightly binaries:
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>hello=tf.constant('Hello, TensorFlow!')
>>>sess=tf.Session()
>>>sess.run(hello)
Hello, TensorFlow!
>>>a=tf.constant(10)
>>>b=tf.constant(32)
>>>sess.run(a+b)
42>>>
##For more information
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.
About
Computation using data flow graphs for scalable machine learning
tensorflow.org
Resources
Readme
Apache-2.0 license
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TensorFlow is an open source software library for numerical computation using
data flow graphs. Nodes in the graph represent mathematical operations, while
the graph edges represent the multidimensional data arrays (tensors) that flow
between them. This flexible architecture lets you deploy computation to one
or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers
working on the Google Brain team within Google's Machine Intelligence research
organization for the purposes of conducting machine learning and deep neural
networks research. The system is general enough to be applicable in a wide
variety of other domains, as well.
If you'd like to contribute to TensorFlow, be sure to review the contribution
guidelines.
We use GitHub issues for
tracking requests and bugs, but please see
Community for general questions
and discussion.
Installation
See Download and Setup for instructions on how to install our release binaries or how to build from source.
People who are a little more adventurous can also try our nightly binaries:
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>hello=tf.constant('Hello, TensorFlow!')
>>>sess=tf.Session()
>>>sess.run(hello)
Hello, TensorFlow!
>>>a=tf.constant(10)
>>>b=tf.constant(32)
>>>sess.run(a+b)
42>>>
##For more information
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.
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Computation using data flow graphs for scalable machine learning
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