From 918f990b3e8287d3001fe31dc060ccca4ef5f6f5 Mon Sep 17 00:00:00 2001
From: Jan Frenzel <jan.frenzel@tu-dresden.de>
Date: Wed, 15 Jun 2022 11:47:42 +0200
Subject: [PATCH] Renamed menu entries for PyTorch, TensorBoard and TensorFlow.
---
doc.zih.tu-dresden.de/docs/software/pytorch.md | 2 +-
doc.zih.tu-dresden.de/docs/software/tensorboard.md | 2 +-
doc.zih.tu-dresden.de/docs/software/tensorflow.md | 4 ++--
doc.zih.tu-dresden.de/mkdocs.yml | 6 +++---
4 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/doc.zih.tu-dresden.de/docs/software/pytorch.md b/doc.zih.tu-dresden.de/docs/software/pytorch.md
index e84f3aac5..4d03aec66 100644
--- a/doc.zih.tu-dresden.de/docs/software/pytorch.md
+++ b/doc.zih.tu-dresden.de/docs/software/pytorch.md
@@ -1,4 +1,4 @@
-# PyTorch
+# Neural Networks with PyTorch
[PyTorch](https://pytorch.org/) is an open-source machine learning framework.
It is an optimized tensor library for deep learning using GPUs and CPUs.
diff --git a/doc.zih.tu-dresden.de/docs/software/tensorboard.md b/doc.zih.tu-dresden.de/docs/software/tensorboard.md
index f7d344807..0dcaf39ad 100644
--- a/doc.zih.tu-dresden.de/docs/software/tensorboard.md
+++ b/doc.zih.tu-dresden.de/docs/software/tensorboard.md
@@ -1,4 +1,4 @@
-# TensorBoard
+# Inspect Model Training with TensorBoard
TensorBoard is a visualization toolkit for TensorFlow and offers a variety of functionalities such
as presentation of loss and accuracy, visualization of the model graph or profiling of the
diff --git a/doc.zih.tu-dresden.de/docs/software/tensorflow.md b/doc.zih.tu-dresden.de/docs/software/tensorflow.md
index 016d6aa25..2329dc12c 100644
--- a/doc.zih.tu-dresden.de/docs/software/tensorflow.md
+++ b/doc.zih.tu-dresden.de/docs/software/tensorflow.md
@@ -1,4 +1,4 @@
-# TensorFlow
+# Neural Networks with TensorFlow
[TensorFlow](https://www.tensorflow.org) is a free end-to-end open-source software library for data
flow and differentiable programming across many tasks. It is a symbolic math library, used primarily
@@ -136,7 +136,7 @@ changing default values for parameters. Thus in some cases, it makes code writte
1.X not compatible with TensorFlow 2.X. However, If you are using the high-level APIs (`tf.keras`)
there may be little or no action you need to take to make your code fully
[TensorFlow 2.0](https://www.tensorflow.org/guide/migrate) compatible. It is still possible to
-run 1.X code, unmodified (except for contrib), in TensorFlow 2.0:
+run 1.X code, unmodified (except for `contrib`), in TensorFlow 2.0:
```python
import tensorflow.compat.v1 as tf
diff --git a/doc.zih.tu-dresden.de/mkdocs.yml b/doc.zih.tu-dresden.de/mkdocs.yml
index e96b6c65e..a9fab6873 100644
--- a/doc.zih.tu-dresden.de/mkdocs.yml
+++ b/doc.zih.tu-dresden.de/mkdocs.yml
@@ -52,9 +52,9 @@ nav:
- Big Data Analytics: software/big_data_frameworks.md
- Machine Learning:
- Overview: software/machine_learning.md
- - TensorFlow: software/tensorflow.md
- - TensorBoard: software/tensorboard.md
- - PyTorch: software/pytorch.md
+ - Neural Networks with TensorFlow: software/tensorflow.md
+ - Inspect Model Training with TensorBoard: software/tensorboard.md
+ - Neural Networks with PyTorch: software/pytorch.md
- Distributed Training: software/distributed_training.md
- Hyperparameter Optimization (OmniOpt): software/hyperparameter_optimization.md
- Machine Learning with PowerAI: software/power_ai.md
--
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