Home.md

-Content:
-- [Introduction](introduction) contains information about the projects, such as expectations and grading.
-- Background
-  - [Server](background/server)
-    - Our Servers
-List of servers
-Reserving GPUs
-Connecting
-OpenVPN
-SSH tunneling
-SSH config
-Setting up your environment on the server
-Pip
-Conda
-Getting data to the server
-Git (favorite)
-Scp for larger files/directories
-Rsync (for example with PyCharm)
-Tmux 
-Usage (detaching, etc)
-Example config
-Nvidia-smi and choosing the appropriate CUDA device
-Htop
+# Content
 
+*   [Introduction](introduction) contains information about the projects, such as expectations and grading.
+*   Background/Basics
+    *   [Server](background/server)
+        *   Our Servers
+            *   List of servers
+            *   Reserving GPUs
+        *   Connecting
+            *   OpenVPN
+            *   SSH tunneling
+            *   SSH config
+        *   Setting up your working environment on the server
+            *   Pip
+            *   Conda
+        *   Getting data to the server
+            *   Git (favorite)
+            *   Scp for larger files/directories
+            *   Rsync (for example with PyCharm)
+        *   Tmux 
+            *   Usage (detaching, etc)
+            *   Example config
+        *   Nvidia-smi and choosing the appropriate CUDA device
+        *   Htop
+    *   [Natural Language Processing](background/nlp)
+        *   Preprocessing
+            *   Tipps
+            *   Tokenizer
+                *   SpaCy / nltk /...
+                *   Byte-Pair-Encoding (sentencepiece / HuggingFace...)
+            *   Vocabulary
+                *   Frequencies/counts are helpful
+        *   Text representations
+            *   Word embeddings
+            *   Bag of words [1,0,0,0,1] 
+        *   Common NLP Architectures
+            *   Encoder-Decoder (Seq2Seq, Tree2Seq, ...)
+            *   Encoder
+            *   Siamese Network
+        *   Transformer
+            *   Attention
+            *   Architecture
+    *   [PyTorch](background/pytorch)
+        *   Basics
+            *   Tensor Operations
+            *   Derive from nn.model, modules etc. 
+            *   Common loss functions
+        *   Use PyTorch Lightning
+        *   Training
+*   [LAVIS Experiment Tipps](experiment_tipps)
 
-Deep Learning
-NLP
-Preprocessing
-Tipps
-Tokenizer
-SpaCy / nltk /...
-Byte-Pair-Encoding (sentencepiece / HuggingFace...)
-Vocabulary
-Frequencies/counts are helpful
-Text representations
-Word embeddings
-Bag of words [1,0,0,0,1] 
-Common NLP Architectures
-Encoder-Decoder (Seq2Seq, Tree2Seq, ...)
-Encoder
-Siamese Network
-Transformer
-Attention
-Architecture
-PyTorch
-Basics
-Tensor Operations
-Derive from nn.model, modules etc. 
-Common loss functions
-Use PyTorch Lightning
-Training
-LAVIS Experimente