Merge branch 'master' of https://github.com/open-source-for-science/T…

…ensorFlow-Tutorials

README.rst

@@ -10,7 +10,7 @@
 This repository aims to provide simple and ready-to-use tutorials for TensorFlow.
 Each tutorial includes ``source code`` and most of them are associated with a ``documentation``.
 
-.. image:: _img/mainpage/TensorFlow_World.gif
+.. .. image:: _img/mainpage/TensorFlow_World.gif
 
 .. The links.
 .. _TensorFlow: https://www.tensorflow.org/install/
@@ -36,7 +36,7 @@ Deep Learning is in very high interest these days - there's a crucial need for r
 
 The strong advantage of TensorFlow is it flexibility in designing highly modular models which can also be a disadvantage for beginners since a lot of the pieces must be considered together when creating the model.
 
-This issue has been facilitated as well by developing high-level APIs such as `Keras <https://keras.io/>`_ and `Slim <https://github.com/tensorflow/models/blob/master/inception/inception/slim/README.md//>`_ which abstract a lot of the pieces used in designing machine learning algorithms.
+This issue has been facilitated as well by developing high-level APIs such as `Keras <https://keras.io/>`_ and `Slim <https://github.com/tensorflow/models/blob/031a5a4ab41170d555bc3e8f8545cf9c8e3f1b28/research/inception/inception/slim/README.md>`_ which abstract a lot of the pieces used in designing machine learning algorithms.
 
 The interesting thing about TensorFlow is that **it can be found anywhere these days**. Lots of the researchers and developers are using it and *its community is growing at the speed of light*! So many issues can be dealt with easily since they're usually the same issues that a lot of other people run into considering the large number of people involved in the TensorFlow community.
 

docs/tutorials/1-basics/basic_math_operations/README.rst

@@ -78,7 +78,7 @@ The ``session``, which is the environment for running the operations, is execute
     # Run the session
     with tf.Session() as sess:
         writer = tf.summary.FileWriter(os.path.expanduser(FLAGS.log_dir), sess.graph)
-        print("output: ", sess.run(welcome))
+        print("output: ", sess.run([a,b,x,y]))
 
     # Closing the writer.
     writer.close()
@@ -94,11 +94,7 @@ The results for running in the terminal is as bellow:
 
 .. code:: shell
 
-        a = 5.0
-        b = 10.0
-        a + b = 15.0
-        a/b = 0.5
-
+        [5.0, 10.0, 15.0, 0.5]
 
 
 If we run the Tensorboard using ``tensorboard --logdir="absolute/path/to/log_dir"`` we get the following when visualiaing the ``Graph``:

docs/tutorials/1-basics/variables/README.rst

@@ -83,7 +83,7 @@ done upon them over the graph, must be initialized or restored from
 saved variables. This only allows us to realize how we can initialize
 specific variables by hand.
 
-Golobal variable initialization
+Global variable initialization
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 All variables can be initialized at once using the
@@ -103,7 +103,7 @@ Both the above methods are identical. We only provide the second one to
 demonstrate that the ``tf.global_variables_initializer()`` is nothing
 but ``tf.variables_initializer`` when you yield all the variables as the input argument.
 
-Initilization of a variables using other existing variables
+Initialization of a variables using other existing variables
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 New variables can be initialized using other existing variables' initial

docs/tutorials/2-basics_in_machine_learning/linear_regression/README.rst

@@ -38,7 +38,7 @@ In order to train the model, the TensorFlow loops through the data and
 it should find the optimal line (as we have a linear model) that fits
 the data. The linear relationship between two variables of X, Y is
 estimated by designing an appropriate optimization problem for which the requirement
- is a proper loss function. The dataset is available from the
+is a proper loss function. The dataset is available from the
 `Stanford course CS
 20SI <http://web.stanford.edu/class/cs20si/index.html>`__: TensorFlow
 for Deep Learning Research.