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YOOOOOOOO CHT SECTION-BY-SECTION PROMPTS 1. 🔬 Abstract “Write a 200-word academic abstract for a research paper on using mathematical optimization and compressed sensing to reduce the size of neural networks without sacrificing performance. Include the key math techniques and AI outcomes.” 2. 📘 Introduction “Write an introduction explaining the problem of neural network bloat, the importance of optimization and sparsity, and how mathematical tools like L1 regularization and compressed sensing can solve this. Include references to recent trends in efficient AI.” 3. 📚 Mathematical Foundation “Explain the mathematical theory of compressed sensing, sparse representations, L1 and L2 norms, and the restricted isometry property (RIP). Provide examples, equations, and intuitive analogies. Use LaTeX formatting for all equations.” 4. 🧠 Model Implementation “Generate Python code (using PyTorch or Scikit-learn) to train a basic neural network on the MNIST dataset, apply L1 regularization to enforce sparsity, and plot model performance vs. sparsity. Include code comments and explanations.” 5. 📈 Results + Graphs “Summarize and visualize experimental results. Plot accuracy vs. sparsity (percentage of non-zero weights). Discuss how different λ (lambda) values in L1 regularization affect accuracy and weight count. Include matplotlib or seaborn code.” 6. 🎯 Conclusion “Write a conclusion that reflects on how mathematical optimization improves AI efficiency, summarizes key results, and proposes future work (e.g. pruning larger models, testing on real-world edge devices, or combining with quantization).” 7. 📖 References “Provide 5–7 academic-style references (real or AI-generated) related to compressed sensing, sparse neural networks, and mathematical optimization in AI. Use APA or IEEE format.” 8. 👨‍💻 Bonus Prompt: Make It a Web App (React) “Generate a React.js layout for displaying a research paper with sections (abstract, intro, math, results, code). Include KaTeX/MathJax integration, code viewer, and Chart.js for graphs.”