Results for “hyperparameter-optimization”
15 skills
nvidia Tao Run Automl
Run automated hyperparameter optimization for NVIDIA TAO models using AutoMLRunner, supporting multiple search algorithms and experiment tracking.
2.2k · bundle
seb1n Hyperparameter Tuning
Optimize machine learning model hyperparameters using grid search, random search, Bayesian optimization, and Hyperband to maximize model performance within a compute budget. Use when the user requests hyperparameter tuning or provides relevant inputs for this workflow.
159
majiayu000 Ml
Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.
567 · bundle
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b4san Performance Optimizer
Transform the agent into a performance engineer. Apply methodologies for measuring, profiling, and optimizing code (caching, algorithm complexity, resource usage).
2
github Power Bi Dax Optimization
Analyzes and optimizes DAX formulas for better performance, readability, and maintainability in Power BI.
36.2k
mcollina Skill Optimizer
Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.
1.9k · bundle
rafsilva85 Credit Optimizer
Reduces AI API costs by 30-75% by classifying task complexity, checking prompt quality, and routing tasks to the most cost-efficient model tier before execution.
49 · bundle
stribus Python Performance Optimization
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
1 · bundle
antigravity Mathguard
Guides AI agents to apply advanced mathematical and probabilistic techniques (Bloom filters, HyperLogLog, FFT, etc.) for large-scale data problems where classical algorithms are optimal but math offers better asymptotic bounds.
42.4k
k-dense-ai Optimize For Gpu
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, and other RAPIDS libraries for dramatic speedups on numerical, data, ML, graph, and simulation workloads.
30.2k · bundle
k-dense-ai Arbor
Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.
30.2k · bundle
muratcankoylan Context Optimization
Extends effective context capacity through strategic compression, masking, caching, and partitioning techniques.
16.9k · bundle
lord1egypt Simpo Training
Trains LLMs with SimPO, a reference-free preference optimization method that outperforms DPO, using configurable hyperparameters and workflows for various models and tasks.
2
eliferjunior Ray
Framework for scaling Python applications from a laptop to a cluster. Includes Ray Core for distributed computing, Ray Serve for model serving, Ray Tune for hyperparameter optimization, and Ray Data for distributed data processing.
0
matlab Matlab Classify Tabular Data
Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).
920 · bundle