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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

Methods of ML

Supervised Learning

Supervised Learning is a method of learning that the data available are both inputs and correct results for those inputs.

Reinforcement Learning

Reinforcement Learning is a method of learning that the data available are only inputs and the measurements how good the outputs are.

Unsupervised Learning

Unsupervised Learning is the method of learning that the data available are only inputs, the model has to figure it out somehow the results should be. Unsupervised Learning is rather hard to apply in practical use cases.

Single Model

Common way to make an ML model.

Boosted Model

Combination of multiple week models into a strong model.

Combinatory Model

Combination of multiple types of small models to make a strong solution.

 
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