ZeroToMastery – AI Mastery: LLMs Explained with Math (Transformers, Attention Mechanisms & More) 2025-4

AI Mastery: LLMs Explained with Math (Transformers, Attention Mechanisms & More), Unlock the secrets behind transformers like GPT and BERT. Learn tokenization, attention mechanisms, positional encodings, and embeddings to build and innovate with advanced AI. Excel in the field of machine learning and become a top-tier AI expert. The Transformer architecture is a foundational model in modern artificial intelligence, particularly in natural language processing (NLP). Introduced in the seminal paper “Attention Is All You Need” by Vaswani et al. in 2017, it is one of the most important technological breakthroughs that gave rise to the Large Language Models you know today like ChatGPT and Claude. What makes Transformers special is that instead of reading word-by-word like old systems (called recurrent models), the Transformer looks at the whole sentence all at once. It uses something called attention to figure out which words are important to focus on for each task. For example, if you’re translating “She opened the box because it was her birthday,” the word “it” might need special attention to understand it refers to “the box.” 1. They Power Modern AI Applications Transformers are the backbone of many AI systems today. Models like GPT, BERT (used in search engines like Google), and DALL·E (image generation) are all based on Transformers. If you’re interested in these technologies, understanding Transformers gives you insight into how they work. Learn how transformers convert raw text into a processable format using techniques like the WordPiece algorithm. Discover the importance of tokenization in enabling language understanding.

What you’ll learn

•  How tokenization transforms text into model-readable data
•  The inner workings of attention mechanisms in transformers
•  How positional encodings preserve sequence data in AI models
•  The role of matrices in encoding and processing language
•  Building dense word representations with multi-dimensional embeddings
•  Differences between bidirectional and masked language models
•  Practical applications of dot products and vector mathematics in AI
•  How transformers process, understand, and generate human-like text

Specificatoin of AI Mastery: LLMs Explained with Math (Transformers, Attention Mechanisms & More)

• Publisher : ZeroToMastery
• Teacher : Patrik Szepesi
• Language : English
• Level : All Levels
• Number of Course : 34
• Duration : 5 hours and 0 minutes

Content of AI Mastery: LLMs Explained with Math (Transformers, Attention Mechanisms & More)

Requirements

• Basic (high-school level) knowledge of Linear Algebra is strongly recommended (basically required in order to really understand everything, but you will still learn lots of high-level information even if you don’t follow the math, which is why we’re only saying “strongly recommended”).

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