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4 changes: 2 additions & 2 deletions site/en/install/pip.md
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ step-by-step instructions.
* {Linux}

Note: Starting with TensorFlow `2.10`, Linux CPU-builds for Aarch64/ARM64
processors are are built, maintained, tested and released by a third party:
processors are built, maintained, tested and released by a third party:
[AWS](https://aws.amazon.com/).
Installing the [`tensorflow`](https://pypi.org/project/tensorflow/)
package on an ARM machine installs AWS's
Expand Down Expand Up @@ -172,7 +172,7 @@ The following NVIDIA® software are only required for GPU support.
instructions may also work for other Linux distros.

Note: Starting with TensorFlow `2.10`, Linux CPU-builds for Aarch64/ARM64
processors are are built, maintained, tested and released by a third party:
processors are built, maintained, tested and released by a third party:
[AWS](https://aws.amazon.com/).
Installing the [`tensorflow`](https://pypi.org/project/tensorflow/)
package on an ARM machine installs AWS's
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2 changes: 1 addition & 1 deletion site/en/install/source.md
Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,7 @@ Use [Git](https://git-scm.com/){:.external} to clone the
<code class="devsite-terminal">cd tensorflow</code>
</pre>

The repo defaults to the `master` development branch. You can also checkout a
The repo defaults to the `master` development branch. You can also check out a
[release branch](https://github.com/tensorflow/tensorflow/releases){:.external}
to build:

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2 changes: 1 addition & 1 deletion site/en/install/source_windows.md
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Expand Up @@ -80,7 +80,7 @@ Use [Git](https://git-scm.com/){:.external} to clone the
<code class="devsite-terminal tfo-terminal-windows">cd tensorflow</code>
</pre>

The repo defaults to the `master` development branch. You can also checkout a
The repo defaults to the `master` development branch. You can also check out a
[release branch](https://github.com/tensorflow/tensorflow/releases){:.external}
to build:

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15 changes: 3 additions & 12 deletions site/en/tutorials/load_data/video.ipynb
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Expand Up @@ -13,6 +13,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "ufPx7EiCiqgR"
},
"outputs": [],
Expand Down Expand Up @@ -90,15 +91,6 @@
"[remotezip](https://github.com/gtsystem/python-remotezip){:.external} to inspect the contents of a ZIP file, [tqdm](https://github.com/tqdm/tqdm){:.external} to use a progress bar, [OpenCV](https://opencv.org/){:.external} to process video files, and [`tensorflow_docs`](https://github.com/tensorflow/docs/tree/master/tools/tensorflow_docs){:.external} for embedding data in a Jupyter notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kWqHj8uOLeja"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -876,8 +868,7 @@
"source": [
"To prepare the data to be fed into the model, use batching as shown below. Notice that when working with video data, such as AVI files, the data should be shaped as a five dimensional object. These dimensions are as follows: `[batch_size, number_of_frames, height, width, channels]`. In comparison, an image would have four dimensions: `[batch_size, height, width, channels]`. The image below is an illustration of how the shape of video data is represented.\n",
"\n",
"![Video data shape](https://www.tensorflow.org/images/tutorials/video/video_data_shape.png)\n",
"\n"
"![Video data shape](https://www.tensorflow.org/images/tutorials/video/video_data_shape.png)\n"
]
},
{
Expand Down Expand Up @@ -943,7 +934,7 @@
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "Video_Loading.ipynb",
"name": "video.ipynb",
"toc_visible": true
},
"kernelspec": {
Expand Down