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
bytebreaker –
My sincere thanks to the author. This is the book I have been hoping to find for four years.
Instead of a bunch of minute details and equations that make me try to recall specifics about math I took years ago, this book provides a useful and wonderful overview of the math concepts that relate to different machine learning algorithms.
If you want to “level up” your intuitive understanding of AI and Machine Learning, this is the book you need.
Bernhard Straub –
Hala Nelson gives a well-structured introduction to the concepts and the mathematical tools that form the basis of AI and data science. Some chapters are tough reading, but the insight and the profound understanding gained from working through the math are well worth the effort.
Some experience with current AI or data analytics tools is helpful for understanding the concepts, but generally the text is self-explanatory.
After reading the book, I feel much more secure in the vast and rapidly expanding field of AI and data science.
Math Customer –
This book outlines the core concepts needed for understanding various AI systems. It gives an accessible introduction to working with data, key statistical concepts, and understanding basic predictive models.The book also unpacks many of the real-world applications of AI, such as neural networks, NLP, and different use-cases across fields. Although the topics are quite nuanced, the author explains them in an impressively understandable way. Highly recommend!
Eduardo Hiroshi Nakamura –
Bom
Explorer –
This is a great book. It provides a great high-level overview of diverse AI-related topics. I am currently a graduate student working on my PhD research.
Although it is written in a simple language, it clarified a lot of the concepts that I had encountered earlier but had doubts.
It clarifies the relation between one type of model and another. For example, SVDs relationship with PCA and with Eigenvalue decomposition. And that too without reading a lot of other technical references and books.
It has some Maths but I wish it had a little more Math or references.
Although a lot of work is done on the NLP chapter, it is a big chapter and it still might have been better to add details to various vector representations.
Overall I like this book to suggest it to my friends.
Placeholder –
For all those exploring the value in AI systems this book is a must have. It explores the math needed behind machine learning systems and shares the concepts in an engaging and digestible way.