Udemy – Introduction to LLMs Transformer,Attention, Deepseek pytorch 2025-3

Udemy – Introduction to LLMs Transformer,Attention, Deepseek pytorch 2025-3 Downloadly IRSpace

Aug 16, 2025 - 07:17 Updated: Aug 17, 2025 - 19:55

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Udemy – Introduction to LLMs Transformer,Attention, Deepseek pytorch 2025-3

Introduction to LLMs Transformer, Attention, Deepseek pytorch course. This course examines how large language models (LLMs) work. In recent years, these models have revolutionized artificial intelligence and have been the foundation for technologies such as ChatGPT and DeepSeek. In this course, we will explore fundamental concepts such as the Attention mechanism and Transformer architecture, which allow models to process text efficiently and understand long-term relationships. We will also analyze Self-Attention, Positional Encodings, and Multi-Head Attention, which play a key role in improving the performance of models. In addition, DeepSeek is introduced as an advanced open-weight model that optimizes Attention mechanisms to deliver competitive performance in natural language processing (NLP). By the end of the course, you will have a comprehensive understanding of training, tuning, and deploying LLMs for various applications. Designed for developers, researchers, and AI enthusiasts, this course will provide you with the knowledge you need to leverage the latest advances in deep learning and NLP.

What you will learn:

How Attention helps models focus on important parts of text.
Understanding Transformers, Self-Attention, and Multi-Head Attention mechanisms.
Exploring how LLMs process, tokenize, and produce human-like text.
Study of DeepSeek architecture and its optimizations for efficiency.
Transformer architecture review.