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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

Models for ML

Almost all models for machine learning is statistics based, expert system is a bit different as it’s based more on known logics.

Biology-based

Neuralnet

Neuralnet (Artificial Neural Network) or ANN is a biology-inspired model for machine learning. Neuralnet is the most common ML model of the modern time; possibly later we may have biology-simulated network instead. This book will use mostly only neuralnet for use in machine learning.
Deep Learning is neuralnet-based learning, either on software or GPU.

Some Abbreviations

ANN — Artificial Neural Network
CNN — Convolutional Neural Network

Known-logic-based

Expert System

Expert system is the learning based a lot on known logics, almost all if-else. Deep Blue was one of those.

Mathematics-based

SVM

SVM (Support Vector Machine) is a mathematics-based model for machine learning which utilise minimal perpendicular vectors for minimisation of distance. SVM is rather limited in solving problems, it is binary classifier. It can not do regression and in order to do multiple classifications, SVM must work in the one-vs-all strategy again and again.

Decision Tree


 
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