Neural Network Debugger

Diagnoses and resolves neural network training failures including gradient issues, loss plateaus, overfitting, and suboptimal hyperparameters. Provides systematic debugging workflows with actionable fixes.

Platinum

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

85.7

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.7

Practical Utility (15%)

8.8

Completeness (10%)

8.5

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.6

Best for

▸Diagnosing vanishing gradient problems in deep transformer networks
▸Resolving training instability in PyTorch multi-GPU setups
▸Fixing learning rate scheduling issues causing loss oscillation
▸Debugging overfitting in computer vision models with proper regularization
▸Troubleshooting NaN losses and exploding gradients in production training

What you'll get

●Gradient flow analysis with layer-wise statistics and specific remediation steps like gradient clipping values
●Learning rate schedule recommendations with mathematical justifications based on loss landscape theory
●Systematic debugging checklist with code examples for common PyTorch/TensorFlow training failures

Not designed for ↓

×Designing neural network architectures from scratch
×Implementing new optimization algorithms or research papers
×General machine learning model selection advice
×Data preprocessing or feature engineering guidance

Expects

Training logs, loss curves, model architecture details, hyperparameter settings, and specific symptoms of training failures.

Returns

Step-by-step diagnostic analysis with specific parameter adjustments, code snippets, and systematic debugging workflows to resolve training issues.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

neural-networksdeep-learningtraining-debugginggradient-issueshyperparameter-tuninglearning-rateoverfittingloss-analysispytorchtensorflowoptimizationmodel-diagnostics

Research Foundation: 8 sources (3 official docs, 4 paper, 1 books)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Works well with

Hyperparameter Tuning ExpertGold ML Experiment TrackerGold MLOps Platform EngineerGold PyTorch Deep Learning EngineerPlatinum TensorFlow/Keras EngineerGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Transformer Architecture ExpertGold Computer Vision Pipeline ArchitectGold PyTorch Lightning EngineerGold

Common Workflows

ML Model Development Pipeline

Complete workflow from fixing training issues to optimizing hyperparameters, tracking experiments, and deploying stable models

neural-network-debugger→Hyperparameter Tuning Expert→ML Experiment Tracker→model-deployment-optimizer

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