DOC: quick fix

doc/api_distributed.md

@@ -24,7 +24,7 @@ on all GPUs.
 ## Python code
 
 We can start from the codebase of the fully convolutional model example 
-described in the OTBTF [Python API tutorial](#api_tutorial.html).
+described in the OTBTF [Python API tutorial](api_tutorial.html).
 
 ### Dataset
 

doc/deprecated.md

@@ -35,4 +35,4 @@ training, etc. is done using the so-called `tensorflow.Strategy`
 
 !!! Note
 
-    Read our [tutorial](#api_tutorial.html) to know more on working with Keras!
\ No newline at end of file
+    Read our [tutorial](api_tutorial.html) to know more on working with Keras!
\ No newline at end of file

doc/docker_build.md

@@ -49,7 +49,7 @@ be a different branch of OTB, bazel cache will help you to rebuild everything
 except TF, even if the docker cache was purged (after `docker 
 [system|builder] prune`).
 In order to recycle the cache, bazel config and TF git tag should be exactly 
-the same, any change in [build-env-tf.sh](build-env-tf.sh) and `--build-arg` 
+the same, any change in *tools/docker/build-env-tf.sh* and `--build-arg` 
 (if related to bazel env, cuda, mkl, xla...) may result in a fresh new build.
 
 Start a cache daemon - here with max 20GB but 10GB should be enough to save 2 

doc/docker_use.md

@@ -104,7 +104,7 @@ Troubleshooting:
 
 If you want to use optimization flags, change GPUs compute capability, etc. 
 you can build your own docker image using the provided dockerfile. 
-See the [docker build documentation](#docker_build.html).
+See the [docker build documentation](docker_build.html).
 
 ## Older images
 

doc/index.md

@@ -16,7 +16,7 @@
 
 
 This remote module of the [Orfeo ToolBox](https://www.orfeo-toolbox.org) 
-provides a generic, multi-purpose deep learning framework, targeting remote 
+provides a generic, multipurpose deep learning framework, targeting remote 
 sensing images processing. It contains a set of new process objects for OTB 
 that internally invoke [Tensorflow](https://www.tensorflow.org/), and new [OTB 
 applications](#otb-applications) to perform deep learning with real-world