ZeroToMastery – Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More) 2025-3

Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More). This course explores the mathematics behind transformer models, attention mechanisms, and other advanced AI concepts. In this course, you’ll uncover the secrets behind transformers like GPT and BERT. Learn tokenization, attention mechanisms, situational encodings, and embeddings to build and innovate with advanced AI. Master machine learning and become a world-class AI expert. Transformer architecture is a fundamental model in modern AI, especially in natural language processing (NLP). What makes transformers special is that instead of reading word by word like older systems (called recurrent models), transformers examine entire sentences at once.

What you will learn:

• How to convert text into readable data for your model with tokenization
• The inner workings of attention mechanisms in transformers
• How to preserve sequence data in AI models with positional encodings
• The role of matrices in language encoding and processing
• Building dense word representations with multidimensional embeddings
• Difference between two-way and masked language models
• Practical applications of inner multiplication and vector mathematics in artificial intelligence
• How Transformers process, understand, and produce human-like text

Who is this course suitable for?

• People who want to gain a deeper understanding of transformer models and LLMs.
• Machine learning professionals who want to advance their skills in advanced artificial intelligence.
• Anyone interested in the mathematics behind artificial intelligence.

Course Details Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More)

• Publisher: ZeroToMastery
• Instructor: Patrik Szepesi
• Training level: Beginner to advanced
• Training duration: 4 hours and 55 minutes
• Number of lessons: 32

Course headings

Advanced AI: LLMs Explained with Math (Transformers, Attention Mechanisms & More) Course Prerequisites

• Familiarity with basic linear algebra strongly recommended

Course images

Sample course video

Download link

Download file – 636 MB

File(s) password: www.downloadly.ir

File size

636 MB