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name: "k9-gh-page"
output_dir: "."
description: |
  It's all about data.
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repository_url: https://github.com/everdark/k9
base_url: https://everdark.github.io/k9/
theme:
collections:
  posts:
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    citations: false
navbar:
  type: default
  title: "k9-data-science"
  left:
    - text: "About Me"
      href: about.html
    - text: "Statistics"
      menu:
        - text: "Bayesian Modeling Explained"
          href: notebooks/stats/bayesian/bayesian_modeling_explained.nb.html
        - text: "---"
        - text: "Bootstrap Sampling 101"
          href: notebooks/stats/bootstrap/bootstrap.nb.html
        - text: "---"
        - text: "Introduction to Topic Models"
          href: notebooks/stats/topic/topic.nb.html
        - text: "---"
        - text: "A Gentle Walkthrough of the Economist's Excess Deaths Model for COVID-19"
          href: notebooks/stats/excess_deaths/excess_deaths.nb.html
        - text: "---"
        - text: "On Linear Regression: Machine Learning vs Econometrics (WIP)"
          #href: notebooks/stats/regression/reg.nb.html
        - text: "---"
        - text: "Linear Mixed Effects Models (WIP)"
          #href: notebooks/stats/mixed_effects/mixed_effects.nb.html
    - text: "Machine Learning"
      menu:
        - text: "Neural Networks Fundamentals"
          href: notebooks/ml/neural_nets/neural_networks_fundamentals.nb.html
        - text: "---"
        - text: "Matrix Factorization for Recommender Systems"
          href: notebooks/ml/matrix_factorization/matrix_factorization.nb.html
        - text: "---"
        - text: "Introduction to Learning-to-Rank"
          href: notebooks/ml/learning_to_rank/learning_to_rank.html
        - text: "---"
        - text: "General-Purpose Ranking Models"
          href: notebooks/ml/neural_ranking/neural_ranking.nb.html
        - text: "---"
        - text: "On Model Explainability"
          href: notebooks/ml/model_explain/model_explain.nb.html
        - text: "---"
        - text: "Demystify Modern Gradient Boosting Trees"
          href: notebooks/ml/gradient_boosting/gbt.nb.html
    - text: "NLU"
      menu:
        - text: "On Subword Units"
          href: notebooks/ml/natural_language_understanding/subword_units/subword_units.nb.html
        - text: "---"
        - text: "Context-Free Word Embeddings (WIP)"
          #href: notebooks/ml/natural_language_understanding/word_embeddings/word_embeddings.nb.html
        - text: "---"
        - text: "Context-Aware Word Embeddings (WIP)"
          #href: notebooks/ml/natural_language_understanding/context_aware_word_embeddings/context_aware_word_embeddings.nb.html
    - text: "Data Engineering"
      menu:
        - text: "Infrastructure-as-Code: Using Terraform to Build a Data Platform on AWS"
        - text: "---"
        - text: "Infrastructure-as-Code: Using Terraform to Deploy AWS Lambda"
        - text: "---"
        - text: "Serverless Deployment: AWS Lambda with HTTP API"
          href: notebooks/data_eng/serverless/lambda_http_api.nb.html
        - text: "---"
  right:
    - text: "Projects"
      menu:
          - text: "YouTube-8M Multi-Label Classification: A TensorFlow 2.0 Journey"
            href: projects/yt8m/yt8m.html
          - text: "---"
          - text: "Visualization for Taiwanese General Election 2020"
            href: projects/tw_election_2020/tw_election_2020.nb.html
          - text: "---"
          - text: "A Note on Model Evaluation for Imbalanced Data"
            href: projects/imbalance_eval/imbalance_eval.html
          - text: "---"
          - text: "A Short Ride on AWS DeepRacer 2020"
            href: projects/deepracer_2020/deepracer_2020.html
          - text: "---"
          - text: "A Simulation Exercise for Your PS5 Lottery Draws"
            href: projects/ps5/ps5.nb.html
          - text: "---"
          - text: "Data Visualization Workshop"
    - text: "Programming"
      menu:
          - text: "Asyncrhonous Programming in R"
            href: notebooks/eng/programming/r/async/async_r.nb.html
          - text: "---"
          - text: "High Performance Computing in R using Rcpp"
            href: notebooks/eng/programming/r/rcpp/rcpp.nb.html
    - icon: fa-linkedin
      href: https://www.linkedin.com/in/kylechung
    - icon: fa fa-rss
      href: index.xml
    - icon: fa-github
      href: https://github.com/everdark/k9
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output: distill::distill_article