Concept & Coding Llm Transformer,Attention, Deepseek Pytorch

How does LLMs works, Understand Concept & Coding of Transformer,Attention, Deepseek using pytorch

What you'll learn

Learn how attention helps models focus on important text parts.

Understand transformers, self-attention, and multi-head attention mechanisms.

Explore how LLMs process, tokenize, and generate human-like text.

Study DeepSeek’s architecture and its optimizations for efficiency.

Explore the transformer architecture

Requirements

python

Description

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!

Overview

Section 1: Introduction

Lecture 1 Introduction to Course

Section 2: Introduction to Transformer

Lecture 2 AI History

Lecture 3 Language as bag of Words

Section 3: Transformer Embedding

Lecture 4 Word embedding

Lecture 5 Vector Embedding

Lecture 6 Types of Embedding

Section 4: Transformer -Encoder Decoder context

Lecture 7 Encoding Decoding context

Lecture 8 Attention Encoder Decoder context

Section 5: Transformer Architecture

Lecture 9 Transformer Architecture with Attention

Lecture 10 GPT vs Bert Model

Lecture 11 Context length and number of Parameter

Section 6: Transformer -Tokenization code

Lecture 12 Tokenization

Lecture 13 Code Tokenization

Section 7: Transformer model and block

Lecture 14 Transformer architecture

Lecture 15 Transformer block

Section 8: Transformer coding

Lecture 16 Decoder Transformer setup and code

Lecture 17 Tranformer model download

Lecture 18 Transformer model code architecture

Lecture 19 Transforme model summary

Lecture 20 Transformer code generate token

Section 9: Attention-Intro

Lecture 21 Transformer attention

Lecture 22 Word embedding

Lecture 23 Positional encoding

Section 10: Attention-Maths

Lecture 24 Attention Math Intro

Lecture 25 Attention Query,Key,Value example

Lecture 26 Attention Q,K,V transformer

Lecture 27 Encoded value

Lecture 28 Attention formulae

Lecture 29 Calculate Q,K transpose

Lecture 30 Attention softmax

Lecture 31 Why multiply by V in attention

Section 11: Attention-code

Lecture 32 Attention code overview

Lecture 33 Attention code

Lecture 34 Attention code Part2

Section 12: Mask Self Attention

Lecture 35 Mask self attention

Section 13: Mask Self Attention code

Lecture 36 Mask Self Attention code overview

Lecture 37 Mask Self Attention code

Section 14: Multimodal Attention

Lecture 38 Encoder decoder transformer

Lecture 39 Types of Transformer

Lecture 40 Multimodal attention

Section 15: Multi-Head Attention

Lecture 41 Multi-Head Attention

Lecture 42 Multi-Head Attention Code Part1

Section 16: Multi-Head Attention code

Lecture 43 Multihead attention code overview

Lecture 44 Multi-head attention encoder decoder attention code

Section 17: Deepseek R1 and R1-zero

Lecture 45 Deepseek R1 training

Lecture 46 Deepseek R1-zero

Lecture 47 Deepseek R1 Architecture

Lecture 48 Deepseek R1 Paper

Section 18: Deepseek R1 Paper

Lecture 49 Deepseek R1 paper Intro

Lecture 50 Deepseek R1 Paper Aha moments

Lecture 51 Deepseek R1 Paper Aha moments Part 2

Section 19: Bonus lecture

Lecture 52 Deepseek R1 summary

Generative AI enthusiasts