Deep Learning Research Paper Author
Budget: ₹1,500 – ₹12,500 INR
I’m preparing a research paper in engineering—specifically computer science—with a focus on deep learning. I have a working outline and key sources, but I need someone who can turn those raw materials into a publication-ready manuscript.
Scope of work:
• Expand my outline into a 4,000–6,000-word paper that follows standard IEEE or ACM formatting.
• Craft a concise abstract, clear problem statement, literature review, methodology, experimental design, results discussion and conclusion.
• Integrate 25–35 up-to-date references, properly cited in-text and in the bibliography.
• Include figures or tables (at least two) to illustrate model architecture, dataset statistics or performance metrics.
• Deliver the full document in Microsoft Word; a LaTeX version is welcome if you’re comfortable with it.
What I’ll provide:
• My outline, key datasets and preliminary results.
• Links to core papers that must be referenced.
• Any clarification you need on the experimental setup.
What I expect back:
• A clearly written, technically rigorous paper that can pass a plagiarism check and be submitted to a peer-reviewed conference or journal.
• All source files and editable diagrams.
• One revision round after initial delivery to incorporate reviewer feedback.
If you have prior publications in engineering or computer science and a solid grasp of deep learning concepts such as CNNs, RNNs or transformers, I’d love to see samples.
Scope of work:
• Expand my outline into a 4,000–6,000-word paper that follows standard IEEE or ACM formatting.
• Craft a concise abstract, clear problem statement, literature review, methodology, experimental design, results discussion and conclusion.
• Integrate 25–35 up-to-date references, properly cited in-text and in the bibliography.
• Include figures or tables (at least two) to illustrate model architecture, dataset statistics or performance metrics.
• Deliver the full document in Microsoft Word; a LaTeX version is welcome if you’re comfortable with it.
What I’ll provide:
• My outline, key datasets and preliminary results.
• Links to core papers that must be referenced.
• Any clarification you need on the experimental setup.
What I expect back:
• A clearly written, technically rigorous paper that can pass a plagiarism check and be submitted to a peer-reviewed conference or journal.
• All source files and editable diagrams.
• One revision round after initial delivery to incorporate reviewer feedback.
If you have prior publications in engineering or computer science and a solid grasp of deep learning concepts such as CNNs, RNNs or transformers, I’d love to see samples.