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Duration 14 hours
Course Outline
Introduction to Google Colab Pro <\/p>
- Distinguishing between Colab and Colab Pro: key features and constraints <\/li>
- Notebook creation and management strategies <\/li>
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Configuration of hardware accelerators and runtime parameters
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Python Programming in the Cloud <\/p>
- Structure of code cells, markdown, and notebooks <\/li>
- Installing packages and configuring development environments <\/li>
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Saving and managing notebook versions via Google Drive
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Data Processing and Visualization <\/p>
- Ingesting and analyzing data from files, Google Sheets, or external APIs <\/li>
- Leveraging Pandas, Matplotlib, and Seaborn for analysis <\/li>
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Handling streams and visualizing large-scale datasets
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Machine Learning with Colab Pro <\/p>
- Applying Scikit-learn and TensorFlow within Colab <\/li>
- Training models utilizing GPU or TPU resources <\/li>
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Assessing and optimizing model performance
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Working with Deep Learning Frameworks <\/p>
- Integrating PyTorch with Colab Pro <\/li>
- Monitoring and managing memory and runtime resources <\/li>
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Saving checkpoints and recording training logs
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Integration and Collaboration <\/p>
- Mounting Google Drive and accessing shared datasets <\/li>
- Facilitating team collaboration through shared notebooks <\/li>
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Exporting content to GitHub or PDF for sharing
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Performance Optimization and Best Practices <\/p>
- Controlling session duration and timeout settings <\/li>
- Organizing code efficiently within notebook structures <\/li>
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Best practices for executing long-running or production-grade tasks
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Summary and Next Steps <\/p>
Requirements
- Proficiency in Python programming <\/li>
- Familiarity with Jupyter notebooks and fundamental data analysis techniques <\/li>
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A solid grasp of standard machine learning workflows
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Target Audience <\/p>
- Data scientists and analysts <\/li>
- Machine learning engineers <\/li>
- Python developers engaged in AI or research initiatives <\/li> <\/ul>
