Essential Math for AI: Next-Level Mathematics for Developing Efficient and Successful AI Systems by Hala Nelson 9781098107581 1098107586

Essential Math for AI: Next-Level Mathematics for Developing Efficient and Successful AI Systems Hala Nelson – Ebook Instant Download/Delivery ISBN(s): 9781098107628, 1098107624, 9781098107581, 1098107586

Product details:

• ISBN 10: 1098107586
• ISBN 13: 9781098107581
• Author: Hala Nelson

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

Table contents:

1. Why Learn the Mathematics of AI?

2. Data, Data, Data

3. Fitting Functions to Data

4. Optimization for Neural Networks

5. Convolutional Neural Networks and Computer Vision

6. Singular Value Decomposition: Image Processing, Natural Language Processing, and Social Media

7. Natural Language and Finance AI: Vectorization and Time Series

8. Probabilistic Generative Models

9. Graph Models

10. Operations Research

11. Probability

12. Mathematical Logic

13. Artificial Intelligence and Partial Differential Equations

14. Artificial Intelligence, Ethics, Mathematics, Law, and Policy

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