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Machine Learning Algorithm Summary
CtrlK
  • Checklist
  • Machine Learning Engineering for Production (MLOps)
    • Introduction to Machine Learning in Production
      • Overview of the ML Lifecycle and Deployment
      • Select and Train a Model
      • Data Definition and Baseline
    • Machine Learning Data Lifecycle in Production
      • Collecting, Labeling and Validating Data
      • Feature Engineering, Transformation and Selection
      • Data journey and Data Storage
      • Advanced labeling, augmentation and data preprocessing
    • Machine Learning Modeling Pipelines in Production
    • Deploying Machine Learning Models in Production
  • Machine Learning Interview Note
    • Introduction
    • Practical ML Techniques/Concepts
    • Search Ranking
    • Feed Based System
    • Recommendation System
    • Ad Prediction System
    • Entity Linking System
  • Recommender Systems
    • Recommender Systems
  • Supervised ML
    • Tree-based Algorithms
      • Decision Tree
      • CART (Classification and regression tree)
      • AdaBoost
      • GBDT
      • Random Forest
      • XGBoost
      • LightGBM
  • Deep Learning Models
    • DNN
    • RNN
    • LSTM/ GRU
    • LSTM with Attention
    • Attention
    • Transformer
    • BERT
  • Graph Models
    • Graph Convolutional Network (GCN) - Background
    • GraphSAGE
    • Graph Attention Network (GAT)
  • Object Detection
    • Table Detection
      • AlexNet
      • VGGNet
      • GoogLeNet
      • ResNet
      • R-CNN
      • Fast R-CNN
      • Faster R-CNN
      • Mask R-CNN
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  1. Machine Learning Engineering for Production (MLOps)
  2. Machine Learning Data Lifecycle in Production

Data journey and Data Storage

PreviousFeature Engineering, Transformation and SelectionNextAdvanced labeling, augmentation and data preprocessing

Last updated 4 years ago

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