Basic Math for AI: A Beginner’s Quickstart Guide to the Mathematical Foundations of Artificial Intelligence (AI Fundamentals)

Price: $4.99
(as of Jan 17,2025 09:20:40 UTC – Details)

ASIN ‏ : ‎ B0D6CBPX2M
Publisher ‏ : ‎ Book Bound Studios (June 5, 2024)
Publication date ‏ : ‎ June 5, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 3021 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 142 pages

Are you interested in delving into the world of Artificial Intelligence (AI) but feeling overwhelmed by the complex mathematical concepts involved? Fear not! In this beginner’s quickstart guide, we will break down the basic math behind AI to help you build a solid foundation for understanding and implementing AI algorithms.

1. Linear Algebra:
Linear algebra plays a crucial role in AI, as it is used to represent and manipulate data. Matrices and vectors are fundamental building blocks in AI, used for tasks such as data transformation, dimensionality reduction, and solving systems of equations. Understanding basic operations like matrix multiplication, addition, and inversion is essential for working with AI algorithms.

2. Calculus:
Calculus is another key mathematical tool used in AI for optimization and modeling. Concepts like derivatives and integrals are used to optimize parameters in machine learning models, such as minimizing error functions or maximizing performance metrics. Understanding how to calculate derivatives and gradients is essential for training neural networks and other AI models.

3. Probability and Statistics:
Probability and statistics play a crucial role in AI for making predictions and decisions based on data. Concepts like probability distributions, hypothesis testing, and statistical inference are used to analyze data, make predictions, and assess the uncertainty of AI models. Understanding key concepts like Bayes’ theorem, central limit theorem, and statistical hypothesis testing is essential for working with AI algorithms.

4. Algorithms and Data Structures:
In addition to mathematical foundations, understanding algorithms and data structures is essential for implementing AI algorithms efficiently. Concepts like sorting algorithms, search algorithms, and data structures like trees and graphs are used in AI for tasks like data manipulation, pattern recognition, and optimization. Understanding how these algorithms work and their computational complexity is crucial for developing efficient AI models.

By mastering these basic mathematical concepts, you will have a solid foundation for understanding and implementing AI algorithms. Whether you are a beginner looking to get started in AI or an experienced professional seeking to deepen your understanding, this quickstart guide will help you build the mathematical foundations necessary for success in the world of Artificial Intelligence. Get ready to dive into the exciting world of AI and unleash your creativity and problem-solving skills!
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