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[New] Concise and Practical AI/ML
  • Pages
    • Preface
    • Artificial Intelligence
      • Concepts
      • High-level Intelligence
    • Maths for ML
      • Calculus
      • Algebra
    • Machine Learning
      • History of ML
      • ML Models
        • ML Model is Better
        • How a Model Learns
        • Boosted vs Combinatory
      • Neuralnet
        • Neuron
          • Types of Neurons
        • icon picker
          Layers
        • Neuralnet Alphabet
        • Heuristic Hyperparams
      • Feedforward
        • Input Separation
      • Backprop
        • Activation Functions
        • Loss Functions
        • Gradient Descent
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      • Design Techniques
        • Normalization
        • Regularization
          • Drop-out Technique
        • Concatenation
        • Overfitting & Underfitting
        • Explosion & Vanishing
      • Engineering Techniques
    • Methods of ML
      • Supervised Learning
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      • Reinforcement Learning
        • Concepts
        • Bellman Equation
        • Q-table
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        • Learning Tactics
          • Policy Network
      • Unsupervised Learning
        • Some Applications
      • Other Methods
    • Practical Cases
    • Ref & Glossary
[New] Concise and Practical AI/ML
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Layers
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Layers

Polylinear Separation

Also polyplanar, and polyhyperplanar separations.
Each neuron does a straight-line separation, and a layer of neurons does polylinear / polyplanar / polyhyperplanar separations; which looks like polygon, polyhedron, polyhyperhedron.

Shape of Layers

The network is learning in the mode called generalisation, which generalise multiple inputs into known-as-the-same-thing, and another group of inputs as another known-as-the-same-thing. However, it is also called summarising in another sense, that the number of values to consider will shrink to fewer and fewer.
And thus, the shape of layers should be having fewer and fewer neurons near the output node.

Polylinear Separation
Shape of Layers
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