academic-plotting

To generate publication-quality figures for ML papers, you can use plotting workflows that create data-driven charts using matplotlib and seaborn or architecture diagrams using Gemini.

To create an architecture diagram for a machine learning system, run the diagram generation workflow with a prompt specifying your components and connections to produce the visual output.

Yes, the data visualization workflow works with matplotlib and seaborn to generate line charts, bar charts, scatter plots, and heatmaps from your experiment results.

Yes, you can use numpy arrays to plot experiment results, as numpy is a required dependency for the data-driven chart generation process.

The best way to visualize model performance metrics for academic writing is using the data visualization workflow to create bar charts comparing different model outputs directly.

Yes, you need the google-genai dependency because it powers the AI-driven architecture diagram generation workflow required for creating system visualizations.