diff --git a/docs/clearml_serving/clearml_serving_tutorial.md b/docs/clearml_serving/clearml_serving_tutorial.md
index abb29a2d..2a03e75f 100644
--- a/docs/clearml_serving/clearml_serving_tutorial.md
+++ b/docs/clearml_serving/clearml_serving_tutorial.md
@@ -26,7 +26,7 @@ Train a model. Work from your local `clearml-serving` repository's root.
     `python3 examples/sklearn/train_model.py`. 
   
 During execution, ClearML automatically registers the sklearn model and uploads it into the model repository. 
-For Manual model registration see [here](#registering-and-deploying-new-models-manually) 
+For information about explicit model registration, see [Registering and Deploying New Models Manually](#registering-and-deploying-new-models-manually). 
 
 ### Step 2: Register Model
 
diff --git a/docs/getting_started/ds/ds_second_steps.md b/docs/getting_started/ds/ds_second_steps.md
index 07e33750..7c7f1a0d 100644
--- a/docs/getting_started/ds/ds_second_steps.md
+++ b/docs/getting_started/ds/ds_second_steps.md
@@ -181,7 +181,7 @@ or check these pages out:
 - Structure your work and put it into [Pipelines](../../pipelines/pipelines.md)
 - Improve your experiments with [Hyperparameter Optimization](../../fundamentals/hpo.md)
 - Check out ClearML's integrations with your favorite ML frameworks like [TensorFlow](../../integrations/tensorflow.md), 
-  [PyTorch](../../guides/frameworks/pytorch/pytorch_mnist.md), [Keras](../../guides/frameworks/keras/keras_tensorboard.md), 
+  [PyTorch](../../integrations/pytorch.md), [Keras](../../integrations/keras.md), 
   and more
 
 ## YouTube Playlist
diff --git a/docs/integrations/pytorch.md b/docs/integrations/pytorch.md
index 75ab2962..da3bc1e1 100644
--- a/docs/integrations/pytorch.md
+++ b/docs/integrations/pytorch.md
@@ -87,7 +87,7 @@ additional tools, like argparse, TensorBoard, and matplotlib:
 
 * [PyTorch MNIST](../guides/frameworks/pytorch/pytorch_mnist.md) - Demonstrates ClearML automatically logging models created with PyTorch, and `argparse` command line parameters
 * [PyTorch with Matplotlib](../guides/frameworks/pytorch/pytorch_matplotlib.md) - Demonstrates ClearML’s automatic logging PyTorch models and matplotlib images. The images are stored in the resulting ClearML experiment's **Debug Samples**
-* [TensorBoard](../guides/frameworks/pytorch/pytorch_tensorboard.md) - Demonstrates ClearML automatically logging PyTorch models, and scalars, debug samples, and text logged using TensorBoard's `SummaryWriter`
+* [PyTorch with TensorBoard](../guides/frameworks/pytorch/pytorch_tensorboard.md) - Demonstrates ClearML automatically logging PyTorch models, and scalars, debug samples, and text logged using TensorBoard's `SummaryWriter`
 * [PyTorch TensorBoard Toy](../guides/frameworks/pytorch/tensorboard_toy_pytorch.md) - Demonstrates ClearML automatically logging debug samples logged using TensorBoard's `SummaryWriter`
 * [PyTorch TensorBoardX](../guides/frameworks/pytorch/pytorch_tensorboardx.md) - Demonstrates ClearML automatically logging PyTorch models, and scalars, debug samples, and text logged using TensorBoardX's `SummaryWriter`
 * [PyTorch Abseil](../guides/frameworks/pytorch/pytorch_abseil.md) - Demonstrates ClearML automatically logging PyTorch models and `absl.flags` parameters
diff --git a/docs/pipelines/pipelines_sdk_function_decorators.md b/docs/pipelines/pipelines_sdk_function_decorators.md
index d0e2b69e..bc100f5b 100644
--- a/docs/pipelines/pipelines_sdk_function_decorators.md
+++ b/docs/pipelines/pipelines_sdk_function_decorators.md
@@ -139,7 +139,7 @@ def step_one(pickle_data_url: str, extra: int = 43):
   
 * Callbacks - Control pipeline execution flow with callback functions 
   * `pre_execute_callback` and `post_execute_callback` - Control pipeline flow with callback functions that can be called 
-    before and/or after a step’s execution. See [here](pipelines_sdk_tasks.md#pre_execute_callback--post_execute_callback).
+    before and/or after a step’s execution. See [here](pipelines_sdk_tasks.md#pre_execute_callback-and-post_execute_callback).
   * `status_change_callback` - Callback function called when the status of a step changes. Use `node.job` to access the 
   `ClearmlJob` object, or `node.job.task` to directly access the Task object. The signature of the function must look like this:
     ```python
diff --git a/docs/pipelines/pipelines_sdk_tasks.md b/docs/pipelines/pipelines_sdk_tasks.md
index 7c0cf541..1df51039 100644
--- a/docs/pipelines/pipelines_sdk_tasks.md
+++ b/docs/pipelines/pipelines_sdk_tasks.md
@@ -166,7 +166,7 @@ pipe.add_function_step(
 * `parents` – Optional list of parent steps in the pipeline. The current step in the pipeline will be sent for execution 
   only after all the parent steps have been executed successfully.
 * `pre_execute_callback` and `post_execute_callback` - Control pipeline flow with callback functions that can be called 
-  before and/or after a step’s execution. See [here](#pre_execute_callback--post_execute_callback).
+  before and/or after a step’s execution. See [here](#pre_execute_callback-and-post_execute_callback).
 * `monitor_models`, `monitor_metrics`, `monitor_artifacts` - see [here](#models-artifacts-and-metrics).
 
 See [add_function_step](../references/sdk/automation_controller_pipelinecontroller.md#add_function_step) for all 
@@ -174,7 +174,7 @@ arguments.
 
 ### Important Arguments
 
-#### pre_execute_callback & post_execute_callback
+#### pre_execute_callback and post_execute_callback
 Callbacks can be utilized to control pipeline execution flow.
 
 A `pre_execute_callback` function is called when the step is created, and before it is sent for execution. This allows a