book1/_toc.yml at main · code-first-ml/book1 · GitHub

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format: jb-book
root: intro
parts:
  - caption: Introduction
    chapters:
      - file: notebooks/introduction/mle_coin
      - file: notebooks/introduction/variational
  - caption: Probability - Univariate Models
    chapters:
      - file: notebooks/probability/Sample_Space_and_Random_Variables
      - file: notebooks/probability/univariate-normal
      - file: notebooks/probability/univariate-normal-expectations
  - caption: Probability - Multivariate Models
    chapters:
      - file: notebooks/probability/mvn-introduction
      - file: notebooks/probability/mvn-marginal
  - caption: Statistics
    chapters:
      - file: notebooks/bayesian_ml/2021-03-23-bayesian-ml
      - file: notebooks/bayesian_ml/2021-04-14-bayesian-linear-regression
      - file: notebooks/bayesian_ml/2021-03-29-bayesian-model-selection
      - file: notebooks/bayesian_ml/2021-03-27-Marginal-Likelihood-2
      - file: notebooks/bayesian_ml/2021-03-31-derivation-of-marginal-likelihood
  - caption: Decision Theory
    chapters:
      - file: notebooks/sampling/monte-carlo
      - file: notebooks/sampling/rejection-sampling-lr
      - file: notebooks/sampling/Metropolis-Hastings
      - file: notebooks/sampling/2021-03-10-importance-sampling
      - file: notebooks/sampling/2021-03-10-rejection-sampling
      - file: notebooks/sampling/2022-02-04-sampling-normal.ipynb
      - file: notebooks/sampling/2020-04-16-inverse-transform.ipynb
      - file: notebooks/sampling/2014-05-01-gibbs-sampling.ipynb
      - file: notebooks/sampling/2014-07-01-mcmc_coins.ipynb
  - caption: Information Theory
    chapters:
      - file: notebooks/graphical_models/2022-02-15-draw-graphical-models.ipynb
  - caption: Linear Algebra
    chapters:
      - file: notebooks/mixture_models/2022-02-14-GMM.ipynb
  - caption: Optimization
    chapters:
      - file: notebooks/information_theory/kl-divergence.ipynb
      - file: notebooks/information_theory/jensen-inequality.ipynb
  # - caption: Linear Discriminant Analysis
  # - caption: Logistic Regression
  # - caption: Linear Regression
  # - caption: Generalized linear models
  - caption: Neural networks for structured data
    chapters:
      - file: notebooks/neural_networks/2020-03-08-keras-neural-non-linear.ipynb
      - file: notebooks/neural_networks/2020-02-28-xor-relu-vector.ipynb
      - file: notebooks/neural_networks/2020-03-02-linear-scratch.ipynb
      - file: notebooks/neural_networks/2018-01-13-denoising.ipynb
  # - caption: Neural networks for Images
  # - caption: Neural networks for Sequences
  # - caption: Exemplar-based methods
  # - caption: Kernel methods
  # - caption: Trees, forests, bagging and boosting
  # - caption: Learning with fewer labeled examples
  # - caption: Dimensionality reduction
  # - caption: Clustering
  - caption: Recommender systems
    chapters:
      - file: notebooks/recommender_systems/2017-12-18-recommend-keras.ipynb
      - file: notebooks/recommender_systems/2017-12-29-neural-collaborative-filtering.ipynb
  # - caption: Graph embeddings
  - caption: References
    chapters:
      - file: notebooks/references/references.md