Robotics AI Thesis Using TensorFlow -- 2
Budget: ₹1,500 – ₹12,500 INR
For my M.Tech in Robotics and AI, I need a full-fledged thesis that explores how artificial intelligence can enhance smart-city services and urban governance. The work must weave together a solid academic narrative with hands-on experimentation in Python, relying on TensorFlow as the main library for model development and analysis.
Your writing should move from an up-to-date literature review into well-structured case studies and end with reproducible simulation results. Along the way, every algorithm needs to be spelled out twice—first as clear pseudocode, then as thoroughly commented Python code. I will use this codebase for live demonstrations, so it must run out of the box and produce the same figures and metrics you report in the thesis.
Deliverables
• Complete thesis document, properly formatted and referenced
• TensorFlow-based Python scripts, notebooks and helper modules
• Pseudocode and flowcharts for each core algorithm
• Simulation results (datasets, plots, logs) packaged for easy rerun
• Slide deck (PPT) that distils the work for an academic defence
• Any additional assets—figures, tables, or appendices—needed to satisfy university guidelines
Acceptance criteria
1. All code executes in Python 3.x with TensorFlow installed via pip.
2. Figures, tables and metrics in the thesis regenerate identically from the supplied scripts.
3. Content passes plagiarism checks and follows standard academic style.
4. Slide deck clearly mirrors the structure and findings of the written thesis.
If you already have experience merging academic writing with real-world TensorFlow projects, you’ll find this assignment straightforward. I’m ready to share formatting templates, grading rubrics and deadlines once we agree on milestones.
Your writing should move from an up-to-date literature review into well-structured case studies and end with reproducible simulation results. Along the way, every algorithm needs to be spelled out twice—first as clear pseudocode, then as thoroughly commented Python code. I will use this codebase for live demonstrations, so it must run out of the box and produce the same figures and metrics you report in the thesis.
Deliverables
• Complete thesis document, properly formatted and referenced
• TensorFlow-based Python scripts, notebooks and helper modules
• Pseudocode and flowcharts for each core algorithm
• Simulation results (datasets, plots, logs) packaged for easy rerun
• Slide deck (PPT) that distils the work for an academic defence
• Any additional assets—figures, tables, or appendices—needed to satisfy university guidelines
Acceptance criteria
1. All code executes in Python 3.x with TensorFlow installed via pip.
2. Figures, tables and metrics in the thesis regenerate identically from the supplied scripts.
3. Content passes plagiarism checks and follows standard academic style.
4. Slide deck clearly mirrors the structure and findings of the written thesis.
If you already have experience merging academic writing with real-world TensorFlow projects, you’ll find this assignment straightforward. I’m ready to share formatting templates, grading rubrics and deadlines once we agree on milestones.