Introduction to LLMs Transformer,Attention, Deepseek pytorch

Welcome to this comprehensive course on how Large Language Models (LLMs) work. In recent years, LLMs have revolutionized the field of artificial intelligence, powering applications like ChatGPT, DeepSeek, and other advanced AI assistants. But how do these models understand and generate human-like text.

In this course, we will break down the fundamental concepts behind LLMs, including attention mechanisms, transformers, and modern architectures like DeepSeek. We will start by exploring the core idea of attention mechanisms, which allow models to focus on the most relevant parts of the input text, improving contextual understanding. Then, we will dive into transformers, the backbone of LLMs, and analyze how they enable efficient parallel processing of text, leading to state-of-the-art performance in natural language processing (NLP).

You will also learn about self-attention, positional encodings, and multi-head attention, key components that help models capture long-range dependencies in text. Beyond the basics, we will examine DeepSeek, a cutting-edge open-weight model designed to push the boundaries of AI efficiency and performance. You'll gain insights into how DeepSeek optimizes attention mechanisms and what makes it a strong competitor to other LLMs.

By the end of this course, you will have a solid understanding of how LLMs work, how they are trained, and how they can be fine-tuned for specific tasks. Whether you're an AI enthusiast, a developer, or a researcher, this course will equip you with the knowledge to work with and build upon the latest advancements in deep learning and NLP. Let's get started.