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[New] Concise and Practical AI/ML
  • Pages
    • Preface
    • Artificial Intelligence
      • Concepts
      • High-level Intelligence
    • Maths for ML
      • Calculus
      • Algebra
    • icon picker
      Machine Learning
      • History of ML
      • ML Models
        • 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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Machine Learning
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This book is mentioning mostly all about gradient-based machine learning, and not other mathematical methods, including perceptron which has no gradient.

Methods of ML

Gradient-based neural network (neuralnet for short) is talked about in this book. Other methods, especially maths-based are mentioned only, eg. Support Vector Machine (SVM), Decision Tree, etc.

Devices for ML

ML models can be trained on CPU, but always better with GPU as GPU has thousands of cores instead of a few cores as in CPU, that many of cores because GPU cores are much simpler just for some maths operators and matrix.

Tasks in ML

Feature Engineering

Prepare data, formatting, etc.

Regular Model Design

Do simple or mid of model complexity, works with big data.

Expert Model Design

Make models of high complexity, works with huge data.

Methods of ML
Devices for ML
Tasks in ML
Feature Engineering
Regular Model Design
Expert Model Design
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