Google Colab has become a go-to platform for data scientists, offering free access to Python notebooks, cloud computation, and collaboration tools. But beyond the basics, there are several hidden features that can make your workflow smoother and more efficient.

🌟 1. Switch Between CPU, GPU, and TPU

Colab allows you to choose your computation hardware depending on the task:

· CPU – Ideal for simple tasks or small datasets.

· GPU – Speeds up machine learning and deep learning model training.

· TPU – Optimized for TensorFlow models, making training extremely fast.

Navigate to Runtime > Change runtime type to switch easily.

🌟 2. Run Shell Commands Directly

You can execute terminal commands without leaving the notebook:

!ls

!pip install numpy

This helps install packages, manage files, or check system info directly in Colab.

🌟 3. Save and Load Notebooks from GitHub

Colab integrates seamlessly with GitHub. You can:

· Open a notebook from a GitHub repo.

· Save changes directly back to GitHub.

· Keep your work version-controlled effortlessly.

🌟 4. Use Hidden Shortcuts for Productivity

Some lesser-known shortcuts include:

· Ctrl+M+B – Add a new code cell below.

· Ctrl+M+A – Add a new code cell above.

· Ctrl+Shift+- – Split a cell into two at the cursor.

These shortcuts save precious time during long coding sessions.

🌟 5. Mount google Drive

Access your google Drive files directly in Colab:

from google.colab import drive

drive.mount('/content/drive')

This is perfect for loading large datasets or saving models.

🌟 6. Form Fields for Parameters

You can turn code cells into interactive forms:

#@param {type:"slider", min:0, max:100, step:1}

x = 50

Great for testing models with different input parameters without rewriting code.

🌟 7. Background Execution

Colab Pro users can run notebooks in the background without keeping the browser open, allowing long-running training or data processing tasks to finish uninterrupted.

🌟 8. Version history and Snapshots

Colab automatically tracks your notebook changes:

· Access revision history via File > Revision history.

· Revert to previous versions if something breaks.

· Snapshots are helpful when experimenting with models.

🌟 9. Magic Commands

Colab supports IPython magic commands to make tasks easier:

· %timeit – Measure code execution time.

· %matplotlib inline – Display plots directly.

· %load – Load code from a file or URL.

🌟 10. Interactive Visualizations

Colab supports Plotly, Bokeh, and other interactive visualization tools, making it easier to explore datasets and build dashboards directly in notebooks.

✅ Conclusion

Google Colab is more than just a cloud notebook — it’s a powerful platform with hidden tools that every data scientist should explore. By leveraging these features, you can save time, improve workflow, and handle complex tasks efficiently.

 

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