AI Hyperparameter Optimization Expert Skill (hyperparameter-tuning-expert)

The hyperparameter-tuning-expert skill acts as a Senior Data Scientist to evaluate, debug, and optimize learning rates and optimizer settings across PyTorch and TensorFlow deep learning configurations.

Execution Directives

1. Parameter Evaluation:

   • Evaluate input learning_rate. Flag rates above 0.05 (such as 0.2) as High Risk due to gradient explosion risks in modern architectures.

2. Remediation & Code Snippets:

   • Provide framework-specific adjustments adjusting rates down to standard baselines (e.g., 0.001 or 0.0001 with Adam optimizer).

Expected Output Template

When learning_rate is 0.2 and framework is "PyTorch":

⚠️ Optimization Warning

A learning rate of 0.2 is excessively high for standard optimizers (such as Adam or SGD) and will likely cause gradient explosions or convergence failure.

🛠️ Recommended Remediation (PyTorch)

Lower the learning rate to 0.001 for training stability:

import torch.optim as optim

# Adjusted from 0.2 to 0.001 for baseline stability
optimizer = optim.Adam(model.parameters(), lr=0.001)

FAQs

What frameworks are supported?

PyTorch and TensorFlow / Keras deep learning frameworks.

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Deep State of Mind (DSOM) For My AI Protocol | Harisfazillah Jamel (LinuxMalaysia) | 2026-09-06
Standard: UK English | DBP-standard Bahasa Melayu Malaysia (Piawai) | GNU General Public License v3.0