Description
Generative AI Explained From Math Basics to LLMs is a generative AI course that bridges the gap between mathematical fundamentals and modern large-scale language models, published by Udemy Online Academy. This course is a comprehensive introduction to generative AI that bridges the gap between mathematical fundamentals and modern large-scale language models. Designed for developers, data scientists, students, and AI enthusiasts, this course explains the essential mathematics behind machine learning before progressing to deep learning, transformer architectures, and generative AI systems. You’ll start with how machines see and prepare data, the types of data that AI works with, numerical challenges like overflow and underflow, and why normalization is essential before learning any model. From there, you will build the mathematical intuition behind AI: vectors, dot products, cosine similarity, and the equation that serves as the backbone of all machine learning.
People will learn linear algebra, probability, calculus concepts, neural networks, attention mechanisms, embeddings, tokenization, training processes, rapid engineering, and the inner workings of large language models (LLMs). Finally, you will get to transformer architecture, the advancement that powers every major generative AI model today. You will learn about tokenization, situational coding, attention mechanisms (including query, key, and value matrices), multi-head attention, and how encoder-only, decoder-only, and encoder-decoder models serve different purposes in the industry.
What you will learn in Generative AI Explained From Math Basics to LLMs:
- Interpret vectors, dot products, cosine similarity, and Euclidean distance, the mathematics behind LLM embeddings
- Explain how model training works: loss functions, gradient descent, and what it really means to learn a machine
- Compare supervised, unsupervised, self-supervised, and reinforcement learning, and how LLMs combine all three
- Review transformer architecture: tokenization, situational coding, attention, and multi-head attention
- Distinguish between encoder-only, decoder-only, and decoder-decoder models and their real-world applications
- Identify key hyperparameters such as learning rate, temperature, top-k, and top-p, and understand their importance
- And…
Course specifications
Publisher: Udemy
Instructors: Omar Koryakin
Language: English
Level: Introductory to Advanced
Number of Lessons: 34
Duration: 4hours and 55minutes
Course topics
Generative AI Explained From Math Basics to LLMs Prerequisites
No programming or coding experience required as this is a fully conceptual course with zero code
Basic arithmetic skills (addition, multiplication, fractions) are helpful but even these are reviewed in the course
A computer or tablet with internet access to stream video lectures so no special software downloads needed
Curiosity about how AI works under the hood so no prior machine learning or data science background necessary
Pictures
Generative AI Explained From Math Basics to LLMs introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
1.6 GB