google-deepmind · Sohail-Hafeez · Jan 13, 2026
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+# Get Started with DeepMind Research
+
+## 1. What is this repo?
+The DeepMind Research repository is a large collection of open source code and implementations that support research papers published by DeepMind, one of the world’s leading AI research organizations. This repo brings together many research projects in machine learning, deep learning, reinforcement learning, and other areas of artificial intelligence, giving developers and students access to real research code.  
+The purpose of this repository is to help the broader research and developer community learn from, experiment with, and build on DeepMind’s work. Instead of just reading research papers, users can explore working code examples, test ideas locally, and understand how complex AI systems are implemented. The projects cover many areas, such as neural network architectures, reinforcement learning benchmarks, graph‑based models, and generative algorithms.  
+Although this repository contains many different projects and can seem overwhelming at first, its strength lies in giving hands on access to real AI research. For anyone interested in advancing their AI skills, this repo provides a valuable bridge between theory and practical code. It also encourages the community to experiment with research concepts, modify implementations, and contribute back improvements or new ideas.
+
+## 2. Setting Up Your Environment
+
+Follow these steps to set up your Python environment and project workspace:
+
+### Step 1: Install Python
+- Download and install the latest stable version of Python: [Click Here to Download](https://www.python.org/downloads/)
+
+### Step 2: Install an IDE
+- Choose a code editor or IDE that you are comfortable with, such as VS Code or PyCharm.  
+- Download VS Code here: [Click Here to Download VS Code](https://code.visualstudio.com/download)
+
+### Step 3: Create a Project Folder
+- Create a folder on your computer where you will keep your project.  
+- Open this folder in your IDE (e.g., VS Code).
+
+### Step 4: Set Up a Virtual Environment
+1. Open a terminal in your IDE.  
+2. Create a virtual environment by running:  
+   ```bash
+   python -m venv venv
+3. Activate the virtual environment:
+   For Linux / Mac:
+   ```bash
+   source venv/bin/activate
+   ```
+   For Windows
+   ```bash
+   venv\Scripts\activate
+   ```
+### Step 5: Install Dependencies
+- Check if there is a requirements.txt file in the project folder; it contains all necessary dependencies.
+- To install all dependencies at once, run:
+````
+pip install -r requirements.txt
+
+````     
+
+### Step 6: Navigate Folders in Terminal
+To go to a subfolder:
+````
+cd yourSubFolderName
+
+````
+To go to a parent folder:
+````
+cd ..
+
+````
+To go to the root folder in
+Windows:
+````
+cd \
+
+````
+To go to the root folder in Linux / Mac:
+````
+cd /
+
+```` 
+## 3. Running Your First Example
+
+Follow these steps to get started with your first project:
+
+### Step 1: Choose a Small Project Folder
+- For beginners, start with a small project folder (`<choose a small project folder>`).  
+- Avoid folders with too many subfolders or files, as they can be overwhelming.  
+- The idea is to start simple and gradually move from basic to advanced projects.
+
+### Step 2: Understand the Project
+- Each folder represents a research project, which is the practical implementation of a research paper.  
+- Before opening any files, look for the `readMe.md` file in the folder.  
+- The `readMe.md` explains the project clearly, answering:
+  - **What** the project does  
+  - **Why** it is important  
+  - **How** it works
+
+### Step 3: Open Jupyter Notebooks (`.ipynb`)
+- If you see a file with the `.ipynb` extension, it is a Jupyter Notebook.  
+- Notebooks are divided into cells, which improves readability and allows you to run parts of the code without executing everything repeatedly.  
+- `.ipynb` files may include:
+  - Model training  
+  - Model architecture  
+  - The full project pipeline  
+- You can open notebooks in [Google Colab](https://colab.research.google.com/) or Kaggle.  
+  - **Tip for beginners:** Google Colab is recommended because it provides free computing resources.  
+- In Colab:
+  1. Click `Connect` in the top-right corner to start a runtime.  
+  2. To use GPU or TPU, click the dropdown next to `Connect`, select `Change runtime type`, and choose your preferred hardware accelerator.
+
+### Step 4: Running Python Files (`.py`)
+- If you encounter a `.py` file:
+  1. Open the folder in your IDE.  
+  2. Navigate to the folder in the terminal using:  
+     ```bash
+     cd yourFileName
+     ```  
+  3. Run the Python file using:  
+     ```bash
+     python yourFileName.py
+     ```
+
+## 4. Key Resources
+
+- **DeepMind Research Papers**  
+  Links to the relevant research papers are provided in the main repository README.
+
+- **AI Learning Resources and Tutorials**
+  - **Machine Learning Crash Course** (by Google):  
+    https://developers.google.com/machine-learning/crash-course
+  - **Papers With Code** (research papers with implementations):  
+    https://paperswithcode.com/
+
+- **DeepMind Research Homepage**  
+  https://deepmind.google/research/
+
+
+## 5. Making the Best Use of the Repository
+
+This repository contains a wealth of research projects, models, and experiments. To fully benefit from it, beginners should follow these strategies:
+
+### Step 1: Understand Before Running
+- Always start by reading the `readMe.md` in each project folder.  
+- Understand the **objective**, **workflow**, and **key components** of the project before running any code.  
+- Identify which parts are **model architecture**, **data processing**, and **evaluation**.  
+
+### Step 2: Use Incremental Learning
+- Begin with simpler projects and smaller datasets to learn the structure and coding style.  
+- Gradually move to advanced models, complex pipelines, and large datasets.  
+- This prevents feeling overwhelmed and builds a strong foundation.
+
+### Step 3: Run Code in a Controlled Environment
+- Use virtual environments to avoid conflicts between dependencies.  
+- Prefer Google Colab or Kaggle for notebooks if your local machine lacks GPU resources.  
+- Run notebooks cell by cell to observe outputs and understand intermediate results.
+
+### Step 4: Experiment and Modify
+- Start small: tweak hyperparameters, change small parts of the model, or modify data samples.  
+- Compare results to understand how changes affect model performance.  
+- This encourages learning through experimentation rather than only observing.
+
+### Step 5: Explore Related Projects
+- After understanding one project, explore related folders to see different approaches to similar problems.  
+- Compare coding styles, model architectures, and evaluation strategies.  
+- This will help you recognize patterns and best practices in research implementations.
+
+### Step 6: Take Notes and Document Learnings
+- Keep a notebook of key insights, challenges, and results.  
+- Documenting your learning will help you retain knowledge and refer back when working on advanced projects.
+
+### Step 7: Collaborate and Ask Questions
+- Discuss ideas, errors, and improvements with peers or online communities.  
+- Collaborating or seeking guidance accelerates learning and deepens understanding.
+
+### Step 8: Contribute Back
+- Once comfortable, you can improve notebooks, fix bugs, or add explanations.  
+- Contributing encourages mastery and helps others learn as well.
+
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