Ebook: Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems
Author: Hala Nelson
- Genre: Computers // Cybernetics: Artificial Intelligence
- Tags: Artificial Intelligence, Machine Learning, Probabilistic Models, Neural Networks, Natural Language Processing, Bayesian Networks, Regression, Decision Trees, Popular Science, Computer Vision, Image Processing, Ethics, Convolutional Neural Networks, Recurrent Neural Networks, Boltzmann Machines, Generative Adversarial Networks, Support Vector Machines, Sentiment Analysis, Graph Data Model, Statistics, Optimization, Partial Differential Equations, Spam Detection, Social Media, Graph Theory, Graph Algorithms, Mathematics
- Year: 2023
- Publisher: O'Reilly Media
- City: Sebastopol, CA
- Edition: 1
- Language: English
- pdf
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 to:
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 to:
- 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
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