Data Science & ML Claude 스킬
data science & ml Claude 스킬을 발견하세요. 53개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
MCMC Sampling with Stan
Implements Bayesian statistical models and runs MCMC sampling using Stan with comprehensive diagnostic validation.
Count Dataset Tokens
Calculates token counts in large-scale datasets using specific tokenizers and precise filtering criteria.
Bayesian Network Fitting and Modification
Streamlines Bayesian Network workflows by guiding structure learning, parameter estimation, causal interventions, and network sampling using industry-standard libraries.
Adaptive Rejection Sampler
Implements efficient adaptive rejection sampling algorithms for generating random samples from log-concave probability distributions.
Video Processing
Guides frame-level analysis and event detection in videos using OpenCV to ensure accurate motion tracking and algorithm validation.
DNA Assembly
Designs optimized primers and validates multi-fragment DNA assembly workflows for Golden Gate and Type IIS cloning techniques.
HuggingFace Model Inference
Deploys pre-trained HuggingFace Transformer models as robust REST API inference services using Flask or FastAPI.
Distribution Search
Finds probability distributions that satisfy specific statistical constraints like KL divergence targets through mathematical analysis and optimized parameterization.
LLM Inference Batching Scheduler
Optimizes LLM inference workloads on compilation-based accelerators by balancing request batching, shape selection, and padding overhead to minimize costs while meeting latency requirements.
DNA Insert Primer Design
Designs specialized primers for inserting DNA sequences into circular plasmids using Q5 site-directed mutagenesis and inverse PCR techniques.
SAM Cell Segmentation
Implements SAM-based biological image segmentation pipelines, converting binary masks to polygon coordinates for microscopy data processing.
Train FastText
Trains and optimizes FastText text classification models while balancing accuracy requirements against model size constraints through systematic hyperparameter tuning and quantization strategies.
PyTorch Model Recovery
Reconstructs PyTorch model architectures from weight files and state dictionaries by analyzing tensor shapes and naming patterns.
ARC-AGI Merge & Pattern Recognition
Manages the merging of conflicting git branches and the development of robust, generalized pattern recognition algorithms for ARC-AGI grid transformation tasks.
SAM Cell Segmentation
Implements advanced image segmentation pipelines using SAM and MobileSAM to extract high-precision cell boundaries and polygon coordinates from images and structured data.
PyTorch Model Recovery
Reconstructs PyTorch model architectures from saved state dictionaries, enables selective layer fine-tuning, and facilitates TorchScript conversion for deployment.
Build Cython Extensions
Provides systematic guidance for building and installing Cython extension packages while resolving compatibility issues with modern Python and NumPy versions.
Raman Spectroscopy Peak Fitting
Analyzes and fits peaks in Raman spectroscopy data using physically-constrained models like Lorentzian, Gaussian, and Voigt functions.
Modernize Scientific Stack
Upgrades legacy Python 2 scientific computing code and analysis pipelines to modern Python 3 standards using contemporary libraries like NumPy and pandas.
Protein Assembly
Designs optimized DNA gBlock sequences for fusion proteins by combining sequences from multiple databases with precise linker and codon constraints.
ReLU Network Model Extraction
Extracts hidden layer weight matrices from black-box ReLU neural networks using input-output query patterns and geometric analysis.
Portfolio Optimization
Optimizes financial computations and portfolio risk metrics by implementing high-performance Python C extensions for large-scale numerical data.
Data Resharding and Partitioning
Reorganizes large-scale datasets into hierarchical directory structures while enforcing strict file size and item count constraints.
Torch Tensor Parallelism
Implements distributed tensor-parallel linear layers in PyTorch to enable training of models that exceed single-device memory limits.
LLM Inference Batching Scheduler
Optimizes LLM inference request grouping and scheduling to minimize operational costs while satisfying latency and padding constraints.
Multi Source Data Merger
Merges heterogeneous data sources into unified datasets using field mappings and priority-based conflict resolution.
Statistical Analysis Toolset
Performs comprehensive hypothesis testing, regression analysis, and Bayesian statistics with automated assumption checking and APA-style reporting.
Pymoo Optimization
Implements advanced multi-objective and many-objective optimization frameworks using state-of-the-art evolutionary algorithms and Pareto analysis.
Medchem Molecular Filters
Applies medicinal chemistry rules and structural alerts to triage and prioritize compound libraries for drug discovery workflows.
Transformers
Integrates state-of-the-art machine learning models for NLP, computer vision, and audio tasks using the Hugging Face ecosystem.
Scroll for more results...