Master’s Image Deep Learning Assignments
Budget: $10 – $20 USD
I need to complete several Master’s-level data-science assignments that revolve around deep learning applied to image or text data. The coursework may ask for anything from image classification and regression to unsupervised clustering, so the solution should be flexible enough to showcase multiple modelling approaches.
The university expects clean, well-commented Python code (Jupyter Notebook format preferred) built with mainstream frameworks such as PyTorch or TensorFlow/Keras. Alongside the code, each task must be accompanied by concise explanations of the data-preprocessing pipeline, model architecture, training procedure, evaluation metrics and a short discussion of the results—written clearly enough for academic submission.
Deliverables for each assignment
• Jupyter Notebook with runnable code and markdown commentary
• Any auxiliary Python modules or scripts needed to reproduce the results
• A brief report (1–2 pages or integrated notebook section) describing methodology, findings and references
• Read-me file with setup instructions and package versions
• Reports as required
Acceptance criteria
• Notebook runs end-to-end on google collab
• Reproducible accuracy or loss values matching the documented results within a reasonable margin
• All figures and tables render correctly without manual intervention
• Evaluation metrics
I will supply the exact assignment prompts and sample datasets once we start, and I’m happy to clarify academic formatting rules or citation styles as needed.
I will have at least 10 such assignments Deliverable within 4 days.
Each assignment must be well explained in a separate file for a beginner to understand and execute.
The university expects clean, well-commented Python code (Jupyter Notebook format preferred) built with mainstream frameworks such as PyTorch or TensorFlow/Keras. Alongside the code, each task must be accompanied by concise explanations of the data-preprocessing pipeline, model architecture, training procedure, evaluation metrics and a short discussion of the results—written clearly enough for academic submission.
Deliverables for each assignment
• Jupyter Notebook with runnable code and markdown commentary
• Any auxiliary Python modules or scripts needed to reproduce the results
• A brief report (1–2 pages or integrated notebook section) describing methodology, findings and references
• Read-me file with setup instructions and package versions
• Reports as required
Acceptance criteria
• Notebook runs end-to-end on google collab
• Reproducible accuracy or loss values matching the documented results within a reasonable margin
• All figures and tables render correctly without manual intervention
• Evaluation metrics
I will supply the exact assignment prompts and sample datasets once we start, and I’m happy to clarify academic formatting rules or citation styles as needed.
I will have at least 10 such assignments Deliverable within 4 days.
Each assignment must be well explained in a separate file for a beginner to understand and execute.