diff --git a/doc.zih.tu-dresden.de/docs/software/machine_learning.md b/doc.zih.tu-dresden.de/docs/software/machine_learning.md
index 7ca0f03502fd82e4e657eb3e4260d4ceaa79fdd7..85a784103d12604afc4126d2b322255a783db2ec 100644
--- a/doc.zih.tu-dresden.de/docs/software/machine_learning.md
+++ b/doc.zih.tu-dresden.de/docs/software/machine_learning.md
@@ -46,9 +46,12 @@ marie@romeo$ module load modenv/scs5
 
 ### Python and Virtual Environments
 
-Python users should use a [virtual environment](python_virtual_environments.md) when conducting machine learning tasks via console.
-In case of using [sbatch files](../jobs_and_resources/batch_systems.md) to send your job you usually
-don't need a virtual environment.
+Python users should use a [virtual environment](python_virtual_environments.md) when conducting
+machine learning tasks via console.
+
+??? hint
+    In case of using [sbatch files](../jobs_and_resources/batch_systems.md)
+    to send your job you usually don't need a virtual environment.
 
 For more details on machine learning or data science with Python see [here](data_analytics_with_python.md).
 
@@ -94,9 +97,9 @@ In the following example, we build a Singularity container with TensorFlow from
 start it:
 
 ```console
-marie@login$ srun -p ml -N 1 --gres=gpu:1 --time=02:00:00 --pty --mem-per-cpu=8000 bash           #allocating resourses from ml nodes to start the job to create a container.
-marie@ml$ singularity build my-ML-container.sif docker://ibmcom/tensorflow-ppc64le             #create a container from the DockerHub with the last TensorFlow version
-marie@ml$ singularity run --nv my-ML-container.sif                                            #run my-ML-container.sif container with support of the Nvidia's GPU. You could also entertain with your container by commands: singularity shell, singularity exec
+marie@login$ srun -p ml -N 1 --gres=gpu:1 --time=02:00:00 --pty --mem-per-cpu=8000 bash    #allocating resourses from ml nodes to start the job to create a container.
+marie@ml$ singularity build my-ML-container.sif docker://ibmcom/tensorflow-ppc64le    #create a container from the DockerHub with the last TensorFlow version
+marie@ml$ singularity run --nv my-ML-container.sif    #run my-ML-container.sif container supporting the Nvidia's GPU. You can also work with your container by: singularity shell, singularity exec
 ```
 
 ## Additional Libraries for Machine Learning
@@ -128,3 +131,20 @@ The following HPC related software is installed on all nodes:
 | IBM XLF Compiler | /opt/ibm/xlf/          |
 | IBM ESSL         | /opt/ibmmath/essl/     |
 | IBM PESSL        | /opt/ibmmath/pessl/    |
+
+## Datasets for Machine Learning
+
+There are many different datasets designed for research purposes. If you would like to download some
+of them, keep in mind that many machine learning libraries have direct access to public datasets
+without downloading it, e.g. [TensorFlow Datasets](https://www.tensorflow.org/datasets). If you
+still need to download some datasets use [DataMover](../../data_transfer/data_mover).
+
+### The ImageNet dataset
+
+The ImageNet project is a large visual database designed for use in visual object recognition
+software research. In order to save space in the file system by avoiding to have multiple duplicates
+of this lying around, we have put a copy of the ImageNet database (ILSVRC2012 and ILSVR2017) under
+`/scratch/imagenet` which you can use without having to download it again. For the future,
+the ImageNet dataset will be available in warm_archive. ILSVR2017 also includes a dataset for
+recognition objects from a video. Please respect the corresponding
+[Terms of Use](https://image-net.org/download.php).
diff --git a/doc.zih.tu-dresden.de/docs/software/tensorflow.md b/doc.zih.tu-dresden.de/docs/software/tensorflow.md
index 0206f3ad18af2db1c0fc165f1e00fc7eb5ddb885..7f03a743ac6acba93bcc02e1c3c4b703bfe9cb38 100644
--- a/doc.zih.tu-dresden.de/docs/software/tensorflow.md
+++ b/doc.zih.tu-dresden.de/docs/software/tensorflow.md
@@ -75,7 +75,7 @@ the notebook by pre-loading a specific TensorFlow module:
 
 ??? hint
     You can also define your own Jupyter kernel for more specific tasks. Please read there
-    documentation about JupyterHub, Jupyter kernels and virtual environments 
+    documentation about JupyterHub, Jupyter kernels and virtual environments
     [here](../../access/jupyterhub/#creating-and-using-your-own-environment).
 
 ## TensorFlow in Containers
@@ -120,3 +120,15 @@ tf.disable_v2_behavior()    #instead of "import tensorflow as tf"
 
 To make the transition to TF 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](keras.io) is a high-level neural network API, written in Python and capable of running on
+top of TensorFlow. Please check the software modules list via
+
+```console
+marie@compute$ module spider Keras
+```
+
+to find out, which Keras modules are available on your partition. TensorFlow should be automatically
+loaded as a dependency. After loading the module, you can use Keras as usual.