From 53171a42ecb8a1d9c3be49c46d6603512073b47e Mon Sep 17 00:00:00 2001 From: Kristal Dale Date: Thu, 4 Oct 2018 16:02:12 -0700 Subject: [PATCH] - Update tutorial title to indicate what it will teach - Simplify introduction - Update CL name substitutions - Update section title "Additional resources" to use standard "Related topics" - Replace "Install and configure Clear Linux Host.." title with "Prerequisites". Update section to be more consistent with other prereq sections e.g. tutorial assumption and instruction to update CL. - Correct section title delineation with correct depth Signed-off-by: Kristal Dale --- .../machine-learning/machine-learning.rst | 47 +++++++++++-------- 1 file changed, 27 insertions(+), 20 deletions(-) diff --git a/source/clear-linux/tutorials/machine-learning/machine-learning.rst b/source/clear-linux/tutorials/machine-learning/machine-learning.rst index a1aff29e..97271e34 100644 --- a/source/clear-linux/tutorials/machine-learning/machine-learning.rst +++ b/source/clear-linux/tutorials/machine-learning/machine-learning.rst @@ -1,22 +1,29 @@ .. _machine-learning: -Machine learning tutorial -######################### +TensorFlow\* machine learning on |CL-ATTR| +########################################## -This tutorial guides you through installing and using a Jupyter\* notebook to -set up and execute a TensorFlow\* machine learning example using the MNIST -data for handwriting recognition using the |CLOSIA|. The initial steps will -have you set up a Jupyter kernel and run a notebook on a bare-metal |CL| -system. +This tutorial will demonstrate the installation and execusion of a TensorFlow\* +machine learning example on |CL-ATTR|. It uses a Jupyter\* Notebook and MNIST +data for handwriting recognition.  -Install and configure a Clear Linux Host OS on bare metal -========================================================= +The initial steps will have you set up a Jupyter kernel and run a Notebook +on a bare-metal |CL| system. -First, follow our instructions to install -:ref:`Clear Linux on bare metal`. +Prerequisites +************* -Once the bare metal installation and initial configuration are complete, add -the following two bundles to your system: +This tutorial assumes you have installed |CL| on your host system. For detailed +instructions on installing |CL| on a bare metal system, follow the +:ref:`bare metal installation tutorial`. + +Before you install any new packages, update |CL| with the following command: + +.. code-block:: bash + + sudo swupd update + +Once your system is updated, add the following bundles to your system: * `machine-learning-web-ui`: This bundle contains the Jupyter application. @@ -33,7 +40,7 @@ directory: sudo swupd bundle-add machine-learning-basic Set up a Jupyter notebook -========================= +************************* With all required packages and libraries installed, set up the file structure for the Jupyter Notebook. @@ -82,7 +89,7 @@ The files needed are: * `t10k-labels-idx1-ubyte.gz`_: Test set labels (4542 bytes) Run the Jupyter machine learning example code -============================================= +********************************************* With |CL|, Jupyter, and TensorFlow installed and configured, we can run the example code. @@ -184,18 +191,18 @@ run the example code. Figure 8: The system's accuracy for the entire data set. -For more in-depth and detailed information on the model used and the -mathematics it entails, visit the TensorFlow tutorials +For more in-depth information on the model used and the mathematics it entails, +visit the TensorFlow tutorials `TensorFlow MNIST beginners demo`_ and `TensorFlow MNIST pros demo`_. **Congratulations!** -You have successfully installed a Jupyter kernel on |CL|. Furthermore, you +You have successfully installed a Jupyter kernel on |CL|. In addition, you trained a neural network to successfully predict the values contained in a data set of hand-written number images. -Additional resources -==================== +Related topics +************** * `MNIST Database website`_ * `TensorFlow MNIST beginners demo`_