Restore "intro" to colabs links - part 2

content/guides/core/reports/_index.md

@@ -42,12 +42,12 @@ Create a collaborative report with a few clicks.
 6. Click **Publish to project**.
 7. Click the **Share** button to share your report with collaborators. 
 
-See the [Create a report](./create-a-report.md) page for more information on how to create reports interactively an programmatically with the W&B Python SDK.
+See the [Create a report](./create-a-report/) page for more information on how to create reports interactively an programmatically with the W&B Python SDK.
 
 ## How to get started
 Depending on your use case, explore the following resources to get started with W&B Reports:
 
 * Check out our [video demonstration](https://www.youtube.com/watch?v=2xeJIv_K_eI) to get an overview of W&B Reports.
-* Explore the [Reports gallery](./reports-gallery.md) for examples of live reports.
-* Try the [Programmatic Workspaces](../../tutorials/workspaces.md) tutorial to learn how to create and customize your workspace.
+* Explore the [Reports gallery](./reports-gallery/) for examples of live reports.
+* Try the [Programmatic Workspaces](../../tutorials/workspaces/) tutorial to learn how to create and customize your workspace.
 * Read curated Reports in [W&B Fully Connected](http://wandb.me/fc).

content/guides/integrations/keras.md

@@ -258,7 +258,7 @@ See our [example repo](https://github.com/wandb/examples) for scripts, including
 
 The `WandbCallback` class supports a wide variety of logging configuration options: specifying a metric to monitor, tracking of weights and gradients, logging of predictions on training_data and validation_data, and more.
 
-Check out [the reference documentation for the `keras.WandbCallback`](../../ref/python/integrations/keras/wandbcallback.md) for full details.
+Check out [the reference documentation for the `keras.WandbCallback`](../../ref/python/integrations/keras/wandbcallback/) for full details.
 
 The `WandbCallback` 
 

content/guides/integrations/pytorch.md

@@ -18,7 +18,7 @@ You can also see our [example repo](https://github.com/wandb/examples) for scrip
 
 ## Log gradients with `wandb.watch`
 
-To automatically log gradients, you can call [`wandb.watch`](../../ref/python/watch.md) and pass in your PyTorch model.
+To automatically log gradients, you can call [`wandb.watch`](../../ref/python/watch/) and pass in your PyTorch model.
 
 ```python
 import wandb
@@ -40,22 +40,22 @@ for batch_idx, (data, target) in enumerate(train_loader):
         wandb.log({"loss": loss})
 ```
 
-If you need to track multiple models in the same script, you can call `wandb.watch` on each model separately. Reference documentation for this function is [here](../../ref/python/watch.md).
+If you need to track multiple models in the same script, you can call `wandb.watch` on each model separately. Reference documentation for this function is [here](../../ref/python/watch/).
 
 {{% alert color="secondary" %}}
 Gradients, metrics, and the graph won't be logged until `wandb.log` is called after a forward _and_ backward pass.
 {{% /alert %}}
 
 ## Log images and media
 
-You can pass PyTorch `Tensors` with image data into [`wandb.Image`](../../ref/python/data-types/image.md) and utilities from [`torchvision`](https://pytorch.org/vision/stable/index.html) will be used to convert them to images automatically:
+You can pass PyTorch `Tensors` with image data into [`wandb.Image`](../../ref/python/data-types/image/) and utilities from [`torchvision`](https://pytorch.org/vision/stable/index.html) will be used to convert them to images automatically:
 
 ```python
 images_t = ...  # generate or load images as PyTorch Tensors
 wandb.log({"examples": [wandb.Image(im) for im in images_t]})
 ```
 
-For more on logging rich media to W&B in PyTorch and other frameworks, check out our [media logging guide](../track/log/media.md).
+For more on logging rich media to W&B in PyTorch and other frameworks, check out our [media logging guide](../track/log/media/).
 
 If you also want to include information alongside media, like your model's predictions or derived metrics, use a `wandb.Table`.
 
@@ -72,7 +72,7 @@ wandb.log({"mnist_predictions": my_table})
 
 {{< img src="/images/integrations/pytorch_example_table.png" alt="The code above generates a table like this one. This model's looking good!" >}}
 
-For more on logging and visualizing datasets and models, check out our [guide to W&B Tables](../tables/intro.md).
+For more on logging and visualizing datasets and models, check out our [guide to W&B Tables](../tables/intro/).
 
 ## Profile PyTorch code