make linter happier

doc.zih.tu-dresden.de/docs/software/machine_learning.md

@@ -62,7 +62,7 @@ The following have been reloaded with a version change:  1) modenv/ml => modenv/
 Python users should use a [virtual environment](python_virtual_environments.md) when conducting
 machine learning tasks via console.
 
-For more details on machine learning or data science with Python see 
+For more details on machine learning or data science with Python see
 [data analytics with Python](data_analytics_with_python.md).
 
 ### R

doc.zih.tu-dresden.de/docs/software/pytorch.md

@@ -20,7 +20,6 @@ and the PyTorch library.
 You can find detailed hardware specification in our
 [hardware documentation](../jobs_and_resources/hardware_taurus.md).
 
-
 ## PyTorch Console
 
 On the **Alpha** partition, load the module environment:

doc.zih.tu-dresden.de/docs/software/tensorflow.md

@@ -62,7 +62,6 @@ marie@ml$ module load TensorFlow
 Module TensorFlow/2.3.1-fosscuda-2019b-Python-3.7.4 and 47 dependencies loaded.
 ```
 
-
 Now we can use TensorFlow. Nevertheless when working with Python in an interactive job, we recommend
 to use a virtual environment. In the following example, we create a python virtual environment and
 import TensorFlow:
@@ -119,7 +118,6 @@ Basic test of tensorflow - A Hello World!!!...
 For further information on TensorFlow in combination with Python see
 [data analytics with Python](data_analytics_with_python.md), for R see [data analytics with R](data_analytics_with_r.md).
 
-
 ## Distributed TensorFlow
 
 For details on how to run TensorFlow with multiple GPUs and/or multiple nodes, see
@@ -142,7 +140,6 @@ tf.disable_v2_behavior()    #instead of "import tensorflow as tf"
 To make the transition to TensorFlow 2.0 as seamless as possible, the TensorFlow team has created
 the tf_upgrade_v2 utility to help transition legacy code to the new API.
 
-
 ## Keras
 
 [Keras](https://keras.io) is a high-level neural network API, written in Python and capable

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