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Concise and Practical AI/ML
  • Pages
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      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
Concise and Practical AI/ML
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Preface
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Preface

THIS IS THE OLD DRAFT OF THE BOOK.
The book aims to provide a concise and practical approach to the knowledge boundary of AI and ML, mainly from the experience of the author at work.
Everything in this book is a try to express the meaning of that thing the core and simple way, however, with a guiding person to read is better. AI is involving multiple algorithms, but the aim of this book will be focused mostly only machine learning and with a try to cover all topics in machine learning.
Some related knowledge related to machine learning are also mentioned, for example, biology and mathematics.
This book will almost always use TensorFlow to make examples, although PyTorch can be academic but TensorFlow is more popular and somehow with better design with activation function inside layer.
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