Deep Learning Literature Review Writer
Budget: $10 – $30 USD
I’m finalising an academic paper that relies heavily on recent advances in Deep Learning, and I need a polished Literature Review and Background section to anchor the study. The review must cover three focal points:
• Vision Transformers (ViTs)
• Graph Convolution Networks (GCNs)
• Convolutional Neural Networks (CNNs)
I already have an outline of my methodology and results; what’s missing is a cohesive narrative that traces how these architectures evolved, the key papers that shaped them, and the open questions my work addresses. Your job is to survey and synthesise the state-of-the-art, cite peer-reviewed sources (preferably within the last five years unless historically significant), and weave the material into a clear, publication-ready section.
IEEE reference style, and a logical flow that moves from general Deep Learning developments to the specifics of ViTs, GCNs, and CNNs. I’ll supply my draft abstract and any figures you might want to reference upon atfer seeing a draft and first payment will be given once i see sample document.
Acceptance criteria
1. Structured literature review and background section ( LaTeX).
2. Minimum 35 up-to-date citations, formatted in IEEE style.
3. Critical comparison of the three architectures, highlighting gaps my research fills.
4. Plagiarism-free and AI free (I will cross-check with Turnitin).
5. One round of revisions included after peer feedback.
• Vision Transformers (ViTs)
• Graph Convolution Networks (GCNs)
• Convolutional Neural Networks (CNNs)
I already have an outline of my methodology and results; what’s missing is a cohesive narrative that traces how these architectures evolved, the key papers that shaped them, and the open questions my work addresses. Your job is to survey and synthesise the state-of-the-art, cite peer-reviewed sources (preferably within the last five years unless historically significant), and weave the material into a clear, publication-ready section.
IEEE reference style, and a logical flow that moves from general Deep Learning developments to the specifics of ViTs, GCNs, and CNNs. I’ll supply my draft abstract and any figures you might want to reference upon atfer seeing a draft and first payment will be given once i see sample document.
Acceptance criteria
1. Structured literature review and background section ( LaTeX).
2. Minimum 35 up-to-date citations, formatted in IEEE style.
3. Critical comparison of the three architectures, highlighting gaps my research fills.
4. Plagiarism-free and AI free (I will cross-check with Turnitin).
5. One round of revisions included after peer feedback.