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Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems
All the math we need to get into AI. Math and AI made easy...
Many industries are eager to integrate AI and data-driven technologies into their systems and operations. But to build truly successful AI systems, you need a firm grasp of the underlying mathematics. This comprehensive guide bridges the gap in presentation between the potential and applications of AI and its relevant mathematical foundations.
In an immersive and conversational style, the book surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models, rather than on dense academic theory. You'll explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more within an exclusive AI context geared toward computer vision, natural language processing, generative models, reinforcement learning, operations research, and automated systems. With a broad audience in mind, including engineers, data scientists, mathematicians, scientists, and people early in their careers, the book helps build a solid foundation for success in the AI and math fields.
You'll be able
Comfortably speak the languages of AI, machine learning, data science, and mathematics Unify machine learning models and natural language models under one mathematical structure Handle graph and network data with ease Explore real data, visualize space transformations, reduce dimensions, and process images Decide on which models to use for different data-driven projects Explore the various implications and limitations of AI
Many industries are eager to integrate AI and data-driven technologies into their systems and operations. But to build truly successful AI systems, you need a firm grasp of the underlying mathematics. This comprehensive guide bridges the gap in presentation between the potential and applications of AI and its relevant mathematical foundations.
In an immersive and conversational style, the book surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models, rather than on dense academic theory. You'll explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more within an exclusive AI context geared toward computer vision, natural language processing, generative models, reinforcement learning, operations research, and automated systems. With a broad audience in mind, including engineers, data scientists, mathematicians, scientists, and people early in their careers, the book helps build a solid foundation for success in the AI and math fields.
You'll be able
Comfortably speak the languages of AI, machine learning, data science, and mathematics Unify machine learning models and natural language models under one mathematical structure Handle graph and network data with ease Explore real data, visualize space transformations, reduce dimensions, and process images Decide on which models to use for different data-driven projects Explore the various implications and limitations of AI
1033 pages, Kindle Edition
Published January 4, 2023
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Displaying 1 of 1 review
June 29, 2023
This was a tough read, I could grok maybe 50% of the content. However in the parts I understood, the explanations of concepts was done well. This book references many current events and research at the time of publishing, so it will be interesting to see how the examples change with future editions. It would have been good to include references by chapter to explore the topics independently. The included references are good, but are still of a level higher than what a beginner would need understanding the concepts.
I hope to get back to re-read some of the chapters in the future.
I hope to get back to re-read some of the chapters in the future.
Displaying 1 of 1 review