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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

Layers in Network

Polylinear Separation

Also polyplanar, and polyhyperplanar separations.
Each neuron does a straight 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.

 
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