Learn GOOGLE-COLAB with Real Code Examples

Visualize data distributions using matplotlib or seaborn

Train a small neural network using TensorFlow

Analyze CSV datasets with pandas

Share a notebook demonstrating data preprocessing steps

Collaborate on research code with peers

Check cell outputs and error messages

Restart runtime to clear memory or GPU cache

Ensure libraries are installed in current runtime

Verify file paths when accessing Drive or uploaded files

Refresh browser if notebook interface misbehaves

Run code cells sequentially

Check outputs and error messages

Validate ML models with test datasets

Monitor GPU/TPU usage

Share notebooks for peer review

Not designed for production web services

Use notebooks for prototyping and testing

Export code to local Python environment for deployment

Save notebooks in GitHub or Drive for versioning

Integrate with ML pipelines externally if needed

Python libraries like TensorFlow, PyTorch, pandas, NumPy, matplotlib

Jupyter notebook interface

Google Drive integration for storage

GPU/TPU acceleration

Markdown support for documentation

Google Drive for file storage and notebook sharing

GitHub for importing/exporting notebooks

BigQuery for large-scale data analysis

TensorFlow and PyTorch for ML tasks

Visualization libraries for plots and charts

Keep notebooks organized with clear headings

Use code and markdown cells effectively

Leverage GPU/TPU for ML tasks

Collaborate via sharing links

Export notebooks for version control and reproducibility

Limited runtime for free-tier sessions

Memory constraints for large datasets

Cannot deploy production apps

Dependent on internet connection

Notebook can become messy with many cells