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Essential Mathematics Behind AI: From Linear Algebra to Neural Networks

Data Science Dojo via YouTube

Overview

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Learn the foundational mathematics behind Artificial Intelligence in this beginner-friendly 54-minute webinar that breaks down complex concepts into digestible explanations. Master essential mathematical components including linear algebra, calculus, probability, and statistics while understanding their practical applications in AI systems. Progress through key topics like plotting data, understanding gradients, calculating derivatives, implementing gradient descent, working with vectors and matrices, and exploring word embeddings for semantic relationships. Gain practical insights into how mathematical concepts translate into real-world AI applications, from pattern recognition to model optimization. Build confidence in approaching AI concepts through clear explanations of mathematical fundamentals, even without an advanced math background. The comprehensive curriculum covers everything from basic principles to advanced applications, including hands-on examples of transforming text into numerical representations and understanding how AI models leverage these mathematical foundations for intelligent decision-making.

Syllabus

- Session Overview
- Role of Mathematics in AI
- The Importance of Mathematics in AI Models
- Linear Algebra: Patterns into Matrices
- Calculus: Optimizing Models for Accuracy
- Plotting Data and Identifying Patterns
- Slope and Intercept of a Line
- Gradients and Their Role in AI
- Calculating Derivatives for Curved Lines
- Gradient Descent: Minimizing Cost Functions
- Vectors and Their Role in AI
- Matrices: Representing Data in AI
- Transforming Text into Numerical Representations
- Word Embeddings and Semantic Relationships
- Applications and Tools

Taught by

Data Science Dojo

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