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[doc] Removes mention of future lab #3154

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May 4, 2024
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[doc] Removes mention of future lab #3154

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15 changes: 1 addition & 14 deletions docs/interactive_tool.md
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Expand Up @@ -73,17 +73,4 @@ No, it’s Java 11, only.
Inspired by Spencer Park’s [IJava project](https://github.com/SpencerPark/IJava), we integrated DJL with Jupyter Notebooks.
For more information on the simple setup, follow the instructions in [DJL Jupyter notebooks](http://docs.djl.ai/docs/demos/jupyter/index.html#setup).
After that, use the Jupyter Notebook freely in your hosted server. You can do all kinds of work, like block building and plotting a graph.
There are [tutorials and instructions](http://docs.djl.ai/docs/demos/jupyter/index.html#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.

## About Future Lab

Future lab is an incubator for future DJL features. We are trying to create a better toolkit/library for Java Developers
getting close to deep learning. We are looking for contributors/testers to explore our latest features.

Here is a list of our ongoing projects (keep updating):

- D2L Java: We are creating a Java implementation for https://d2l.ai book.
- NLP Word Embedding: We are looking for more word embedding portal that can be used to encode/decode.

If you are interested, please feel free to let us know on Slack or simply file an issue saying you are interested to participate.
We will send bi-weekly updates to you for the feature we are working-in-progress.
There are [tutorials and instructions](http://docs.djl.ai/docs/demos/jupyter/index.html#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.
2 changes: 0 additions & 2 deletions docs/quick_start.md
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Expand Up @@ -18,8 +18,6 @@ You can also view our 1.5 hour long (in 8 x ~10 minute segments) DJL 101 tutoria
We developed several interactive toolkits for you to experience DJL.
You can start from here to run inference online and download starter template with DJL.

See [DJL Future Labs](interactive_tool.md)

## Beginner tutorial

To get started, we recommend that you follow our short [beginner tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/index.html). It takes you through some of the basics of deep learning to create a model, train your model, and run inference using your trained model.
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