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tools machine learning AI 技能

发现 tools machine learning 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。

分类目录: tools machine learning

显示第 337–360 个,共 1,317 个

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GitHub

pricing

Piebald-AI

Guide for updating model pricing in Splitrail. Use when adding new AI model costs or updating existing pricing data.

GitHub

langchain4j-ai-services-patterns

giuseppe-trisciuoglio

Build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered fe...

GitHub

neural-network-design

aj-geddes

Design and architect neural networks with various architectures including CNNs, RNNs, Transformers, and attention mechanisms using PyTorch and TensorFlow

GitHub

classification-modeling

aj-geddes

Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification

GitHub

feature-engineering

aj-geddes

Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability

GitHub

ml-model-explanation

aj-geddes

Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability

GitHub

time-series-analysis

aj-geddes

Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models

GitHub

computer-vision

aj-geddes

Implement computer vision tasks including image classification, object detection, segmentation, and pose estimation using PyTorch and TensorFlow

GitHub

ml-pipeline-automation

aj-geddes

Build end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins

GitHub

recommendation-engine

aj-geddes

Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches

GitHub

anomaly-detection

aj-geddes

Identify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring

GitHub

dimensionality-reduction

aj-geddes

Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency

GitHub

recommendation-system

aj-geddes

Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement

GitHub

model-deployment

aj-geddes

Deploy machine learning models to production using Flask, FastAPI, Docker, cloud platforms (AWS, GCP, Azure), and model serving frameworks

GitHub

sentiment-analysis

aj-geddes

Classify text sentiment using NLP techniques, lexicon-based analysis, and machine learning for opinion mining, brand monitoring, and customer feedback analysis

GitHub

regression-modeling

aj-geddes

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

GitHub

causal-inference

aj-geddes

Determine cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy evaluation and treatment effects

GitHub

model-monitoring

aj-geddes

Monitor model performance, detect data drift, concept drift, and anomalies in production using Prometheus, Grafana, and MLflow

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