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Essential math for ai: next-level mathematics for efficient and successful ai systems,...

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Описание

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 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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