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+# Getting Started with DeepMind Research
+
+Welcome. This repository contains implementations and example code that accompany DeepMind publications. Whether you are a student, researcher, or AI enthusiast, this guide will help you explore and run projects in this repository.
+
+## What is in this repository
+
+This repository hosts more than 70 research projects from DeepMind publications. These projects cover areas such as:
+
+- Machine Learning: graph networks, transformers, generative models
+- Reinforcement Learning: Deep Q Networks, offline reinforcement learning, multi agent systems
+- Computer Vision: object detection, image generation, video prediction
+- Computational Biology: AlphaFold, protein structure prediction, genomics
+- Physics Simulation: graph neural networks for physics, mesh-based simulation
+- Scientific Computing: quantum chemistry, climate modeling, fusion control
+
+Each directory in this repository represents a self-contained project. Every project includes its own README, code, and sometimes datasets.
+
+## Prerequisites
+
+Before you begin, make sure you have:
+
+- Python 3.7 or above (most projects use Python 3.8 or 3.9)
+- Git for cloning the repository
+- Basic command line knowledge
+- Familiarity with Python and ML libraries such as NumPy, TensorFlow, PyTorch, or JAX
+- Optional: a GPU for computationally intensive projects
+
+Recommended tools:
+
+- Virtual environment manager: venv, conda, or virtualenv
+- Code editor: VS Code, PyCharm, or Jupyter Notebook
+- Package manager: pip or conda
+
+## Quick start
+
+### Step 1. Clone the repository
+
+```bash
+git clone https://github.com/google-deepmind/deepmind-research.git
+cd deepmind-research
+
+### Step 2. Choose a project
+
+Browse the repository folders and pick a project that interests you. Each project has its own directory with a dedicated README.
+
+### Step 3. Set up the environment
+
+Each project can have different dependencies. Go into the project directory and follow the setup steps in its README.
+
+Example: running the Graph Matching Networks project
+
+```bash
+cd graph_matching_networks
+
+python3 -m venv gmn_env
+source gmn_env/bin/activate
+
+# On Windows:
+# gmn_env\Scripts\activate
+
+pip install -r requirements.txt
+```
+
+After installing dependencies, follow the project README instructions to run scripts or notebooks.
+
+### Step 4. Run your first example
+
+Most projects include example scripts or Jupyter notebooks. Look for files such as:
+
+* demo.py
+* example.ipynb
+* train.py
+* eval.py
+
+Check the project README for the exact command to run.
+
+## Recommended projects for beginners
+
+Start with these well-documented, beginner-friendly projects.
+
+### Easiest to get started
+
+1. PrediNet (PrediNet folder)
+
+   * Why: simple Colab notebook, no local setup required
+   * Topics: neural networks, relational reasoning
+   * How to run: open the Colab link in the README
+
+2. Bootstrap Your Own Latent (BYOL) (byol folder)
+
+   * Why: clear documentation and visual results
+   * Topics: self-supervised learning, computer vision
+   * Prerequisites: JAX and Haiku
+
+3. Graph Matching Networks (graph_matching_networks folder)
+
+   * Why: well structured code and example scripts
+   * Topics: graph neural networks, similarity learning
+   * Prerequisites: TensorFlow and Sonnet
+
+### Intermediate projects
+
+4. Learning to Simulate (learning_to_simulate folder)
+
+   * Why: comprehensive examples and interesting applications
+   * Topics: physics simulation, graph networks
+   * Prerequisites: TensorFlow, and a basic understanding of particle systems
+
+5. MeshGraphNets (meshgraphnets folder)
+
+   * Why: real-world applications in engineering
+   * Topics: graph networks, physics simulation
+   * Prerequisites: JAX, and understanding of mesh-based methods
+
+6. Adversarial Robustness (adversarial_robustness folder)
+
+   * Why: practical ML security applications
+   * Topics: adversarial training, robustness
+   * Prerequisites: JAX or PyTorch
+
+### Advanced projects
+
+7. AlphaFold CASP13 (alphafold_casp13 folder)
+
+   * Why: groundbreaking research, complex but rewarding
+   * Topics: structural biology, deep learning
+   * Prerequisites: TensorFlow, domain knowledge helps
+
+8. Enformer (enformer folder)
+
+   * Why: state-of-the-art genomics model
+   * Topics: genomics, transformers
+   * Prerequisites: TensorFlow, genomics background helps
+
+## Learning paths
+
+Path 1. Computer vision and deep learning
+
+1. Start with BYOL for self supervised learning
+2. Explore BigBiGAN for generative models
+3. Try Hierarchical Probabilistic U Net for medical imaging
+
+Path 2. Graph neural networks
+
+1. Begin with Graph Matching Networks
+2. Move to Learning to Simulate
+3. Advance to MeshGraphNets
+
+Path 3. Reinforcement learning
+
+1. Learn from Side Effects Penalties
+2. Try Option Keyboard for hierarchical reinforcement learning
+3. Experiment with RL Unplugged for offline reinforcement learning
+
+Path 4. Scientific computing
+
+1. Start with Learning to Simulate for physics
+2. Explore Fusion TCV for plasma control
+3. Dive into AlphaFold for biology
+
+## Common dependencies
+
+Many projects use these frameworks:
+
+* JAX: [https://github.com/google/jax](https://github.com/google/jax)
+* Haiku: [https://github.com/deepmind/dm-haiku](https://github.com/deepmind/dm-haiku)
+* Sonnet: [https://github.com/deepmind/sonnet](https://github.com/deepmind/sonnet)
+* TensorFlow: [https://www.tensorflow.org/](https://www.tensorflow.org/)
+* PyTorch: [https://pytorch.org/](https://pytorch.org/)
+
+Install them as needed:
+
+```bash
+pip install jax jaxlib
+pip install dm-haiku
+pip install dm-sonnet
+pip install tensorflow
+pip install torch torchvision
+```
+
+## Useful resources
+
+DeepMind resources:
+
+* DeepMind Publications: [https://deepmind.com/research/publications/](https://deepmind.com/research/publications/)
+* DeepMind Blog: [https://deepmind.com/blog](https://deepmind.com/blog)
+* DeepMind GitHub: [https://github.com/deepmind](https://github.com/deepmind)
+
+Learning resources:
+
+* JAX Documentation: [https://jax.readthedocs.io/](https://jax.readthedocs.io/)
+* TensorFlow Tutorials: [https://www.tensorflow.org/tutorials](https://www.tensorflow.org/tutorials)
+* PyTorch Tutorials: [https://pytorch.org/tutorials/](https://pytorch.org/tutorials/)
+* Distill: [https://distill.pub/](https://distill.pub/)
+* Papers with Code: [https://paperswithcode.com/](https://paperswithcode.com/)
+
+Community:
+
+* GitHub Discussions: [https://github.com/google-deepmind/deepmind-research/discussions](https://github.com/google-deepmind/deepmind-research/discussions)
+* GitHub Issues: [https://github.com/google-deepmind/deepmind-research/issues](https://github.com/google-deepmind/deepmind-research/issues)
+
+## Troubleshooting
+
+ModuleNotFoundError
+
+* Make sure you installed the project dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+CUDA or GPU errors
+
+* Install GPU enabled versions of JAX, TensorFlow, or PyTorch, or run on CPU.
+
+Python version mismatch
+
+* Check the project README for the required Python version and use a matching environment.
+
+Out of memory errors
+
+* Reduce batch size, use a smaller model, or use a machine with more RAM or VRAM.
+
+Getting help:
+
+1. Check the project README
+2. Search existing issues
+3. Open a new issue and include environment details and error logs
+4. Ask in Discussions for general questions
+
+## Contributing
+
+Interested in contributing? Check CONTRIBUTING.md.
+
+Ways to contribute:
+
+* Report bugs by opening an issue
+* Improve documentation by fixing typos or clarifying instructions
+* Share results by discussing experiments and findings
+* Add examples by sharing working code
+
+Note: You may need to sign a Contributor License Agreement for code contributions.
+
+## Repository structure
+
+```text
+deepmind-research/
+README.md
+CONTRIBUTING.md
+GET_STARTED.md
+LICENSE
+alphafold_casp13/
+learning_to_simulate/
+meshgraphnets/
+70 plus other projects/
+```
+
+Each project directory is self contained with its own README, code, and dependencies.
+
+## Tips for success
+
+1. Start simple, do not jump into the most complex project first
+2. Read the paper, understanding the research helps you understand the code
+3. Use virtual environments, keep dependencies isolated per project
+4. Take notes, document what you learn as you explore
+5. Experiment, modify parameters and see what happens
+6. Ask questions, use Discussions and Issues
+
+## Next steps
+
+1. Pick a beginner project
+2. Read the project README
+3. Set up your environment
+4. Run the example scripts or notebooks
+5. Experiment with parameters and datasets
+6. Share your findings with the community
+
+## Feedback
+
+Found this guide helpful? Have suggestions for improvement? Please open an issue or contribute via a pull request.
+
+This is not an official Google product.
+
+```
+```