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

In most of the simple problems to solve, classic algorithms can give perfect and exact results. However, for complex cases, classic algorithms can’t give perfect result any more and the results are just roughly.
A machine learning model can be better than classic algorithm as it is fast. Imagine a classic algorithm functioning at certain order of complexity, eg. O(n^m), will be slower than ML model with simple feedforward only for inference, and the results are both the same, roughly-accurate results.
The more complex the problem to solve is, the better feedforward (inference) will out-perform classic algorithms.
An ML model takes a lot of time to learn, but the inference is fast.

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