Deep Neural Architecture Consultation -- 2
Budget: $250 – $750 USD
I’m finalising a research paper that relies on a deep neural network, yet the current methodology feels dated and under-optimised. My chief aim is to refine the model architecture so the study stands on solid, modern ground before submission.
Here’s what I need from you:
• A critical, line-by-line review of the “Methods” section with concrete suggestions on how to restructure or augment the existing fully connected network (e.g., layer depth, activation choices, regularisation, skip connections, modular blocks).
• Clear justification for each proposed change, backed by recent literature or empirical reasoning, so I can reference it in the manuscript.
• Guidance on any ancillary adjustments the new architecture demands—data flow revisions, hyper-parameter tweaks, or revised training schedules.
• A short summary (bullet or paragraph form) that I can drop straight into the paper, properly cited, outlining why the updated architecture is superior.
I’m comfortable with TensorFlow or PyTorch terminology, so feel free to present your ideas in either framework. If we need to hop on a quick call to clarify edge cases or data constraints, that’s fine; otherwise, annotated comments in a shared doc work perfectly.
The deliverable is your reviewed manuscript section plus the architecture recommendations ready for immediate incorporation. Let’s make this methodology airtight.
Here’s what I need from you:
• A critical, line-by-line review of the “Methods” section with concrete suggestions on how to restructure or augment the existing fully connected network (e.g., layer depth, activation choices, regularisation, skip connections, modular blocks).
• Clear justification for each proposed change, backed by recent literature or empirical reasoning, so I can reference it in the manuscript.
• Guidance on any ancillary adjustments the new architecture demands—data flow revisions, hyper-parameter tweaks, or revised training schedules.
• A short summary (bullet or paragraph form) that I can drop straight into the paper, properly cited, outlining why the updated architecture is superior.
I’m comfortable with TensorFlow or PyTorch terminology, so feel free to present your ideas in either framework. If we need to hop on a quick call to clarify edge cases or data constraints, that’s fine; otherwise, annotated comments in a shared doc work perfectly.
The deliverable is your reviewed manuscript section plus the architecture recommendations ready for immediate incorporation. Let’s make this methodology airtight.