You will get Implement & Debug a Neural Network in PyTorch in 5 Days
Project details
Need a neural network implemented in PyTorch — from a paper, a spec, or an existing design? I've written 12,000+ lines of PyTorch across 131 scripts, building everything from CNNs (LeNet through ResNet) to Transformers, energy-based models, and generative models (VAE, GAN).
What you get:
• Clean, documented PyTorch code (no Jupyter-notebook spaghetti)
• Training pipeline with configurable hyperparameters
• Evaluation metrics and loss visualization
• GPU-ready — local RTX 4070 Ti (12 GB VRAM) for typical workloads; cloud GPU scaling available for larger models
I don't do "quick and dirty" — every implementation comes with proper training/validation splits, early stopping, and model checkpointing. Have a specific paper or architecture in mind? Message me first to confirm scope.
What you get:
• Clean, documented PyTorch code (no Jupyter-notebook spaghetti)
• Training pipeline with configurable hyperparameters
• Evaluation metrics and loss visualization
• GPU-ready — local RTX 4070 Ti (12 GB VRAM) for typical workloads; cloud GPU scaling available for larger models
I don't do "quick and dirty" — every implementation comes with proper training/validation splits, early stopping, and model checkpointing. Have a specific paper or architecture in mind? Message me first to confirm scope.
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, PyTorch, SciPyWhat's included
| Service Tiers |
Starter
$200
|
Standard
$400
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 2 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $200
Additional Revision
+$30
Additional Model Variation
(+ 3 Days)
+$150Frequently asked questions
About Xiukai
Physics PhD | Python & PyTorch | PyTorch Model Development
Beijing, China - 10:47 pm local time
During my PhD and postdoc (2018–2025) at the Chinese Academy of Sciences, my research involved statistical modeling, MCMC methods, and computational problem-solving. I hold 18 patents from this work. My research has produced 11 peer-reviewed publications with 140+ citations. My statistical foundation includes MCMC sampling, convergence diagnostics (R-hat, ESS), and uncertainty quantification — applied daily in both research and engineering contexts. I write clean, documented code and prefer clear text-based communication. Currently open to fixed-price Python/data/scientific computing projects and long-term PyTorch/deep learning work. Also known as Lucien. Most development runs on my local Linux workstation (16-core CPU, 64 GB RAM, RTX 4070 Ti 12 GB VRAM) — no cloud markup for typical workloads. For projects needing more compute, I can scale up with cloud GPUs.
Steps for completing your project
After purchasing the project, send requirements so Xiukai can start the project.
Delivery time starts when Xiukai receives requirements from you.
Xiukai works on your project following the steps below.
Revisions may occur after the delivery date.
Review model spec & confirm scope
I review your paper/spec, assess feasibility, and confirm timeline and deliverables with you.
Implement model & training pipeline
Build the architecture in PyTorch with data loading, hyperparameter config, and training loop.