Most AI math books are written by mathematicians for mathematicians. However, this one is completely different. Written from an engineering perspective (think in terms of building blocks), it connects theory to practice with visualizations, Python code, and real life applications. You will explore not just what the math is but why it matters and where it is applied in real life. In less than a week it got over 20K views, and since then it is only growing!
What is included in this book:
> The Architecture of Mathematics: How math connects from foundations to AI, including Gödel's paradoxes and Einstein's relativity
> The Field of Artificial Intelligence: From Control Theory to modern AI, understanding symbolic vs. non-symbolic AI approaches
> Linear Algebra: Vectors, matrices, determinant, eigenvalues, and transformations that show geometry of data in machine learning
> Multivariable Calculus: Limits, Derivatives, integrals, and how change in many directions powers backpropagation
> Probability & Statistics: Bayesian methods, distributions, and Markov models for learning from uncertainty
> Optimization Theory: Gradient descent, Adam optimizer, and how machines learn step by step
> Real-World Applications: A lot of Python code examples, animated visualizations, and practical examples of where the math is applied
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