fix typo

sphinx/source/tutorial-bigdata-ai.rst

@@ -310,7 +310,6 @@ You can download the example as `tutorial.examples.spark-cluster-with-r <https:/
 
 #. Components in the infrastructure connect to each other, therefore several port ranges must be opened for the VMs executing the components. Clouds implement port opening various way (e.g. security groups for OpenStack, etc). Make sure you implement port opening in your cloud for the following port ranges:
 
-# TODO
    ===========     =============  ====================
    Protocol        Port(s)        Service
    ===========     =============  ====================
@@ -471,7 +470,7 @@ Apache Spark cluster with Python Stack
 Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. For more information visit the `official Apache Spark page <https://spark.apache.org>`_ .
 
 Apache Spark cluster together with HDFS (Hadoop Distributed File System) represents one of the most important tool for Big Data and machine learning applications, enabling the parallel processing of large data sets on many virtual machines, which are running Spark workers. On the other hand, setting up a Spark cluster with HDFS on clouds is not straightforward, requiring deep knowledge of both cloud and Apache Spark architecture. To save this hard work for scientists we have created and made public the required infrastructure descriptors by which Occopus can automatically deploy Spark clusters with the number of workers specified by the user.
-Spark also provides a special library called “Spark MLlib” for supporting machine learning applications. Similarly, to the R-oriented Spark environment, we have masteroped the infrastructure descriptors for the creation of a machine learning environment in the cloud. Here, the programming language is Python and the user programming environment is Jupyter. The complete machine learning environment consists of the following components: Jupyter, Python, Spark and HDFS. Deploying this machine learning environment is also automatically done by Occopus and the number of Spark workers can be defined by the user.
+Spark also provides a special library called “Spark MLlib” for supporting machine learning applications. Similarly, to the R-oriented Spark environment, we have developed the infrastructure descriptors for the creation of a machine learning environment in the cloud. Here, the programming language is Python and the user programming environment is Jupyter. The complete machine learning environment consists of the following components: Jupyter, Python, Spark and HDFS. Deploying this machine learning environment is also automatically done by Occopus and the number of Spark workers can be defined by the user.
 
 This tutorial sets up a complete Apache Spark infrastructure integrated with HDFS, Python and Jupyter Notebook. It contains a Spark Master node and Spark Worker nodes, which can be scaled up or down.
 
@@ -606,9 +605,9 @@ This tutorial sets up a complete Apache Spark infrastructure integrated with HDF
 TensorFlow and Keras with Jupyter Notebook Stack
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and masteropers easily build and deploy ML powered applications. TensorFlow was masteroped by the Google Brain team for internal Google use. It was released under the Apache License 2.0 on November 9, 2015. For more information visit the `official TensorFlow page <https://tensorflow.org/>`_ .
+TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and masteropers easily build and deploy ML powered applications. TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache License 2.0 on November 9, 2015. For more information visit the `official TensorFlow page <https://tensorflow.org/>`_ .
 
-Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was masteroped with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier. In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. For more information visit the `official Keras  page <https://keras.io>`_ .
+Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier. In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. For more information visit the `official Keras  page <https://keras.io>`_ .
 
 The complete machine learning environment consists of the following components: Jupyter, Keras (version 2.2.4) and TensorFlow (version 1.13.1).
 
@@ -656,7 +655,6 @@ The complete machine learning environment consists of the following components:
 
 #. Services on the virtual machine should be available from outside, therefore some port numbers must be opened for the VM executing the components. Clouds implement port opening various way (e.g. security groups for OpenStack, etc). Make sure you implement port opening in your cloud for the following port ranges:
 
-# TODO
    ===========     =============  ====================
    Protocol        Port(s)        Service
    ===========     =============  ====================
@@ -713,9 +711,9 @@ The complete machine learning environment consists of the following components:
 TensorFlow and Keras with Jupyter Notebook Stack using NVIDIA GPU card
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and masteropers easily build and deploy ML powered applications. TensorFlow was masteroped by the Google Brain team for internal Google use. It was released under the Apache License 2.0 on November 9, 2015. For more information visit the `official TensorFlow page <https://tensorflow.org/>`_ .
+TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and masteropers easily build and deploy ML powered applications. TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache License 2.0 on November 9, 2015. For more information visit the `official TensorFlow page <https://tensorflow.org/>`_ .
 
-Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was masteroped with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier. In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. For more information visit the `official Keras  page <https://keras.io>`_ .
+Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier. In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. For more information visit the `official Keras  page <https://keras.io>`_ .
 
 The complete machine learning environment consists of the following components: Jupyter, Keras and TensorFlow utilizing the power of a GPU card.
 
@@ -767,7 +765,6 @@ The complete machine learning environment consists of the following components:
 
 #. Services on the virtual machine should be available from outside, therefore some port numbers must be opened for the VM executing the components. Clouds implement port opening various way (e.g. security groups for OpenStack, etc). Make sure you implement port opening in your cloud for the following port ranges:
 
-# TODO
    ===========     =============  ====================
    Protocol        Port(s)        Service
    ===========     =============  ====================