add a tutorial table

microsoft · Oct 31, 2024 · ef657de · ef657de
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 Then, we can use the familiar PyTorch-like syntax to conduct the optimization.
 
+## Tutorials
+
+| **Level** | **Tutorial**                                                                                     | **Run in Colab**                                                                                                                                                                                        | **Description**                                                                                                                                                                       |
+| --- |--------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| 
+| Beginner | [**Getting Started**](https://microsoft.github.io/Trace/quickstart/quick_start.html)             | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/microsoft/Trace/blob/website/docs/quickstart/quick_start.ipynb)        | Introduces basic primitives like `node` and `bundle`. Showcases a code optimization pipeline.                                                                                         |
+| Beginner | [**Adaptive AI Agent**](https://microsoft.github.io/Trace/quickstart/quick_start_2.html)         | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/microsoft/Trace/blob/website/docs/quickstart/quick_start_2.ipynb)      | Introduce primitive `model` that allows anyone to build self-improving agents that react to environment feedback. Shows how an LLM agent learns to place a shot in a Battleship game. 
+| Intermediate | [**Multi-Agent Collaboration**](https://microsoft.github.io/Trace/quickstart/virtualhome.html)   | N/A                                                                                                                                                                                                     | Demonstrates how Trace can be used for multi-agent collaboration environment in Virtualhome.                                                                                          
+| Intermediate | [**NLP Prompt Optimization**](https://microsoft.github.io/Trace/examples/nlp/bigbench_hard.html) | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/microsoft/Trace/blob/website/docs/examples/nlp/bigbench_hard.ipynb) | Shows how Trace can optimizes prompt and code together jointly for BigBench-Hard 23 tasks.                                                                                            
+| Advanced | [**Robotic Arm Control**](https://microsoft.github.io/Trace/examples/robotics/metaworld.html)        | [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/microsoft/Trace/blob/website/docs/examples/robotics/metaworld.ipynb)                                      | Trace can optimize code to control a robotic arm after observing a full trajectory of interactions.                                                                                   |
+
+
 ## Supported Optimizers
 
 Currently, we support three optimizers: