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Concise and Practical AI/ML
  • Pages
    • Preface
    • What are AI and ML
    • Mathematics Recap
      • Calculus
      • Algebra
    • Libraries to Use
    • Models for ML
    • Methods of ML
    • Neuralnet Alphabet
    • Neuralnet
      • Neuron
        • Types of Neurons
        • Input Separations
        • Activation Functions
      • Layers in Network
      • Loss Functions
      • Gradient Descent
      • Feedforward
      • Backpropagation
      • Optimisers & Training
      • Techniques in ML
        • Normalisation
        • Regularisation
        • Concatenation
        • Boosted & Combinatory
        • Heuristic Hyperparams
      • Problems in Neuralnet
        • Overfitting
        • Explosion and Vanishing
    • Supervised Learning
      • Regression
      • Classification
    • Reinforcement Learning
      • Concepts
      • Learning Tactics
      • Policy Network
      • Bellman Equation
      • Q-table
      • Q-network
    • Unsupervised Learning
      • Some Applications
    • Incremental Learning
    • Case Studies
      • Algorithm Approximator
      • Regression
      • Classification
      • Sequence Learning
      • Pattern Learning
      • Generative
    • Notable Mentions
Concise and Practical AI/ML
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Boosted & Combinatory
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Boosted & Combinatory

Boosted Model

Multiple weak models to form a strong model.

Combinatory Model

Multiple models of different network designs to form a solution.

Distributed Learning

The training data are split and spread over multiple computers and join together in 1 model.

Federated Learning

It’s kinda combinatory learning but instead of multiple models, it is multiple computers to join the learning process together.

Boosted Model
Combinatory Model
Distributed Learning
Federated Learning
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