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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

- 1. Why Learn the Mathematics of AI
- 2. Data, Data, Data
- Data for AI
- Real Data vs. Simulated Data
- Mathematical Models: Linear vs. Nonlinear
- An Example of Real Data
- An Example of Simulated Data
- Mathematical Models: Simulations and AI
- Where Do We Get our Data From?
- The Vocabulary of Data Distributions, Probability, and Statistics
- Continuous Distributions vs. Discrete Distributions (Density vs. Mass)
- The Power Of The Joint Probability Density Function
- Distribution of Data: The Uniform Distribution
- Distribution of Data: The Bell Shaped Normal (Gaussian) Distribution
- Distribution of Data: Other Important and Commonly Used Distributions
- The Various Uses Of The Word Distribution
- Summary And Looking Ahead