• Hands-On Machine Learning with R Book Club
  • Welcome
    • Book club meetings
    • Pace
  • 1 Introduction to Machine Learning
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 2 Modeling Process
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 3 Feature & Target Engineering
    • Introduction
    • 3.2 Target engineering
    • 3.3 Dealing with missingness
    • 3.1 Missing values treatment
    • Imputation
    • 3.4 Feature filtering
    • 3.5 Numeric feature engineering
    • 3.6 Categorical feature engineering
    • One-hot & dummy encoding
    • Alternatives
    • 3.7 Dimension reduction
    • 3.8 Proper implementation
    • Data leakage
    • Putting the pieces together
    • Meeting Videos
      • Cohort 1
  • 4 Linear Regression
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 5 Logistic Regression
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 6 Regularized Regression
    • Introduction
    • 6.1 - Why regularize?
    • 6.2.1 - Ridge penalty
    • 6.2.2 - Lasso penalty
    • 6.2.3 - Elastic nets
    • 6.3 - Implementation
    • 6.4 - Tuning
    • 6.5 - Feature interpretation
    • 6.6 - Attrition data
    • 6.7 - Final thoughts
    • Meeting Videos
      • Cohort 1
  • 7 Multivariate Adaptive Regression Splines (MARS)
    • Extending linear models
    • Explaning the model
    • Loading prerequisites
      • 7.0.1 Libraries to use
      • 7.0.2 Data to use
    • Explaning model’s summary
    • Tuning Process
      • 7.0.3 Caret
      • 7.0.4 Tidymodels
    • Feature interpretation
    • Final thoughts
    • Meeting Videos
      • Cohort 1
  • 8 K-Nearest Neighbors
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 9 Decision Trees
    • 9.1 - Introduction
    • 9.2 - Structure
    • 9.3 - Partitioning
    • 9.4 - How deep?
    • 9.4.1 - Early stopping
    • 9.4.2 - Prunning
    • 9.5 - AMES Housing example
    • 9.6 - Feature Interpretation
    • 9.7 - Final thoughts
    • 9.1 BONUS: Attrition (decision tree classifier)
    • Meeting Videos
      • Cohort 1
  • 10 Bagging (Bootstrap Aggregating)
    • It isn’t always useful
    • Out-of-bag (OOB) error
    • Prerequisites
      • 10.0.1 Packages to load
      • 10.0.2 Data to use
    • Training bagging trees
    • Number of trees and error estimate
    • Parallelize training
    • Error curve for custom parallel bagging
    • Feature interpretation
    • Final thoughts
    • Meeting Videos
      • Cohort 1
  • 11 Random Forests
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 12 Gradient Boosting
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 13 Deep Learning
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 14 Support Vector Machines
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 15 Stacked Models
    • Base idea
    • Setting enviroment
    • 15.1 Process Description
    • 15.2 R packages available
    • 15.3 Model Training Process
    • 15.4 Alternative Model Training Process
    • 15.5 Automated machine learning
    • Meeting Videos
      • Cohort 1
  • 16 Interpretable Machine Learning
    • 16.1 Introduction
    • 16.2 Setting enviroment
      • 16.2.1 Loading libraries
      • 16.2.2 Getting the data
      • 16.2.3 Training the model
      • 16.2.4 Defining Local Observations to Explain
    • 16.3 Interpretation trade-off
    • 16.4 Model-specific implementation
    • 16.5 Permutation-based feature importance
      • 16.5.1 Concept
      • 16.5.2 Implementation
    • 16.6 Partial dependence
      • 16.6.1 Concept
      • 16.6.2 Implementation
      • 16.6.3 Adding feature importance
    • 16.7 Individual conditional expectation
      • 16.7.1 Concept
      • 16.7.2 Implementation
    • 16.8 Feature interactions
      • 16.8.1 Implementation
      • 16.8.2 Alternatives
    • 16.9 LIME
      • 16.9.1 Implementation
    • 16.10 Shapley values
      • 16.10.1 Implementation
    • 16.11 Localized step-wise procedure
      • 16.11.1 Implementation
    • Meeting Videos
      • Cohort 1
  • 17 Principal Components Analysis
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 18 Generalized Low Rank Models
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 19 Autoencoders
    • 19.1 Main Concept
    • 19.2 Applications
    • 19.3 Prerequisites
    • 19.4 Undercomplete autoencoders
      • 19.4.1 Comparing PCA to an autoencoder
      • 19.4.2 Coding example
      • 19.4.3 Stacked autoencoders
      • 19.4.4 Tuning hidden layers configuration
      • 19.4.5 Anomaly detection
    • 19.5 Denoising autoencoders
      • 19.5.1 Corruption process
      • 19.5.2 Coding example
    • 19.6 Sparse autoencoders
      • 19.6.1 Mathematical description
      • 19.6.2 Tuning sparsity \(\beta\) parameter
    • 19.7 Alternative autoencoders
    • 19.8 Additinal references
    • Meeting Videos
      • Cohort 1
  • 20 K-means Clustering
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 21 Hierarchical Clustering
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 22 Model-based Clustering
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
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Hands-On Machine Learning with R Book Club

19.8 Additinal references

  • Lenny #2: Autoencoders and Word Embeddings: https://ayearofai.com/lenny-2-autoencoders-and-word-embeddings-oh-my-576403b0113a

  • Autoencoder: https://en.wikipedia.org/wiki/Autoencoder#Variational_autoencoder_(VAE)

  • Understanding Variational Autoencoders (VAEs): https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73

  • Introduction to Different Activation Functions for Deep Learning: https://medium.com/@shrutijadon/survey-on-activation-functions-for-deep-learning-9689331ba092