Essential Math for AI

Book description

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory.

Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field.

  • Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more
  • Learn how to adapt mathematical methods to different applications from completely different fields
  • Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions

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Table of contents

  1. 1. Why Learn the Mathematics of AI
    1. What Is AI?
    2. Why Is AI so Popular Now?
    3. What Is AI Able To Do?
      1. An AI Agent’s Specific Tasks
    4. What Are AI’s Limitations?
    5. What Happens When AI Systems Fail?
    6. Where Is AI Headed?
    7. Who Are Currently The Main Contributors To The AI Field?
    8. What Math Is Typically Involved In AI?
    9. Summary And Looking Ahead
  2. 2. Data, Data, Data
    1. Data for AI
    2. Real Data vs. Simulated Data
    3. Mathematical Models: Linear vs. Nonlinear
    4. An Example of Real Data
    5. An Example of Simulated Data
    6. Mathematical Models: Simulations and AI
    7. Where Do We Get our Data From?
    8. The Vocabulary of Data Distributions, Probability, and Statistics
    9. Continuous Distributions vs. Discrete Distributions (Density vs. Mass)
    10. The Power Of The Joint Probability Density Function
    11. Distribution of Data: The Uniform Distribution
    12. Distribution of Data: The Bell Shaped Normal (Gaussian) Distribution
    13. Distribution of Data: Other Important and Commonly Used Distributions
    14. The Various Uses Of The Word Distribution
    15. Summary And Looking Ahead
  3. 3. Fitting Functions to Data
    1. Traditional And Very Useful Machine Learning Models
    2. Numerical Solutions vs. Analytical Solutions
    3. Regression: Predict A Numerical Value
      1. Training Function
      2. Loss Function
      3. Optimization
    4. Logistic Regression: Classify Into Two Classes
      1. Training function
      2. Loss Function
      3. Optimization
    5. Softmax Regression: Classify Into Multiple Classes
      1. Training function
      2. Loss Function
      3. Optimization
    6. Incorporating The Above Models Into The Last Layer Of A Neural Network
    7. Other Popular Machine Learning Techniques and Ensembles of Techniques
      1. Support Vector Machines
      2. Decision Trees
      3. Random Forests
    8. Performance measures for classification models
    9. Summary and Looking Ahead
  4. About the Author

Product information

  • Title: Essential Math for AI
  • Author(s): Hala Nelson
  • Release date: December 2022
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098107567