Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Optimizes numerical linear algebra computations for finding eigenvalues of small dense matrices through direct LAPACK integration and overhead reduction.
Implements distributed tensor parallel operations in PyTorch, focusing on efficient weight sharding and collective operations across multiple GPUs.
Provides procedural guidance for setting up HuggingFace model inference services using Flask, covering environment setup, model caching, and robust API implementation.
Accesses the UniProt knowledgebase to search, retrieve, and map protein sequence and functional information.
Automates end-to-end geophysical data inversion pipelines from mesh generation through 3D visualization.
Accesses the European Nucleotide Archive to retrieve genomic sequences, raw reads, and metadata for bioinformatics pipelines.
Infers gene regulatory networks from expression data using high-performance machine learning algorithms like GRNBoost2 and GENIE3.
Builds and deploys serverless bioinformatics workflows using the Latch Python SDK and cloud infrastructure.
Accesses the NIH Metabolomics Workbench to query metabolite data, study metadata, and standardized nomenclature for biomarker discovery.
Implements comprehensive machine learning workflows including classification, regression, and data preprocessing using the industry-standard Scikit-learn library.
Implements standalone command-line inference tools in C, C++, and Rust by extracting weights and logic from PyTorch models without Python dependencies.
Accesses the world's largest chemical database to retrieve compound properties, structures, and bioactivity data for cheminformatics workflows.
Processes mass spectrometry data for proteomics and metabolomics analysis using the pyOpenMS library.
Queries ChEMBL's vast database of bioactive molecules and drug discovery data for medicinal chemistry research.
Applies machine learning to chemistry, biology, and materials science to predict molecular properties and design new compounds.
Manages large-scale N-dimensional arrays with chunking and compression for high-performance scientific computing and cloud storage.
Performs advanced astronomical data analysis, coordinate transformations, and cosmological calculations using the industry-standard Astropy library.
Accelerates Python numerical computations by implementing performance-critical mathematical algorithms as high-speed C extensions.
Optimizes FastText text classification models by balancing hyperparameter tuning with strict file size and accuracy constraints.
Optimizes dominant eigenvalue calculations for small dense matrices by reducing Python wrapper overhead through direct LAPACK integration.
Guides the compilation of the Caffe deep learning framework from source and the execution of CIFAR-10 image classification training workflows.
Guides the compilation of the legacy Caffe deep learning framework and the training of convolutional neural networks on the CIFAR-10 dataset.
Calculates token counts in large-scale datasets using specific tokenizers and precise filtering criteria.
Streamlines Bayesian Network workflows by guiding structure learning, parameter estimation, causal interventions, and network sampling using industry-standard libraries.
Implements efficient adaptive rejection sampling algorithms for generating random samples from log-concave probability distributions.
Guides frame-level analysis and event detection in videos using OpenCV to ensure accurate motion tracking and algorithm validation.
Optimizes LLM inference workloads on compilation-based accelerators by balancing request batching, shape selection, and padding overhead to minimize costs while meeting latency requirements.
Manages the merging of conflicting git branches and the development of robust, generalized pattern recognition algorithms for ARC-AGI grid transformation tasks.
Implements advanced image segmentation pipelines using SAM and MobileSAM to extract high-precision cell boundaries and polygon coordinates from images and structured data.
Provides systematic guidance for building and installing Cython extension packages while resolving compatibility issues with modern Python and NumPy versions.
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