Understanding the Fundamental Principles of Large Language Models (LLMs)

Name: Understanding the Fundamental Principles of Large Language Models (LLMs)
Price: 74250 KRW
Rating: 3.5 (2 reviews)

Explains the basic principles of large language models like ChatGPT, focusing on theory.

(3.5) 2 reviews

72 learners

arigaram

llm

llm성능평가및튜닝

chatgpt

생성형ai

NLP

gpt

ChatGPT

LLM

What you will learn!

Fundamental Principles of Large Language Models (LLM)
LLM Development Process

My translated book "Deep Learning for Natural Language Processing" (by Stephan Raaijmakers, translated by Park Jin-su, published by Sigma Press, 2024) has been selected as a recommended book in the academic category of the Sejong Books. This is wonderful news following "Hands-On Generative Adversarial Networks with PyTorch 1.x" (Wikibooks) selected in 2020, and "Practical Electronics for Everyone" (J-Pub) selected as an excellent academic book by the National Academy of Sciences of the Republic of Korea in 2019. To share this joy together, we are holding a 25% discount event from November 5th to November 18th.

🧭Change History

September 27, 2025
- "Section 17. 'Understanding the Complete LLM Development Process' Advanced", "
  I have significantly expanded and reorganized the lesson outline for "Section 18. 'Understanding the Complete LLM Development Process' Hands-on Practice (Python + Google Colab)". I am preparing lecture content that aligns with the new outline.
September 18, 2025
- I added the precautions to the detailed introduction page.
- I have revised the table of contents for "Section 10, 'Transformer Architecture' Hands-on Practice." I am preparing lecture content that aligns with the new table of contents.
- I have revised the table of contents for "Section 16, Understanding the Complete LLM Development Process." Accordingly, I am deleting the existing lectures and preparing new lecture content that aligns with the new table of contents.
September 1, 2025
- I have categorized all classes with bullet points as [Basic], [Advanced], and [Practice]. The existing [Supplementary] classes correspond to [Advanced] classes, so I have labeled them with the '[Advanced]' bullet point.
- To reduce confusion and make the learning process easier to understandI have divided all sections into general sections (sections that include [Basic] classes or [Advanced] classes), advanced sections (sections that only include [Advanced] classes), and practice sections (sections that only include [Practice] classes).
- By reducing the possibility of confusion in this way, we have made all classes that were changed to private status on August 22, 2025, public again.
August 31, 2025
- I have released the practice table of contents for Section 1 through Section 10. I plan to release the content gradually over time.
- I have re-published the [Supplementary] and [Advanced] class syllabi for Sections 1 through 10. This is to help students understand the connection with the practical exercise syllabus.
August 22, 2025
- We have changed the lessons in the [Advanced] and [Supplementary] courses that are not yet completed to private status. We plan to make each section public as they are completed in the future. This measure is to reduce confusion for students, so we would appreciate your understanding.
August 17, 2025
- We are currently adding advanced course classes and dividing classes with long lecture times. Therefore, the section numbers in the course materials and the section numbers shown in the table of contents may differ.

🔥 Course Features

"Today's learning becomes tomorrow's competitive edge! The most practical course to build AI expertise that will shine even 10 years from now."
"Worth more than 100,000 won? No. This is an investment in AI capabilities that will protect your career even 10 years from now."
"Stop with superficial knowledge! Through bonus lectures, you can learn deep into LLM technology."
"It's different from other courses. It covers everything from the latest research trends to future AI."
"Grow as an AI expert while developing responsible AI development capabilities! Learn ethics, regulations, and safety all at once."

After taking the course

You will be able to clearly explain the fundamentals of the technology based on a deep understanding of the definition and characteristics of generative artificial intelligence, as well as the principles of language models.
You can understand the entire LLM development process from data collection to preprocessing, model selection, training, evaluation, and maintenance.
You will be able to understand the process of creating language models in a theory-focused manner, enabling you to solve specific problems using pre-training, transfer learning, fine-tuning, and RLHF (Reinforcement Learning from Human Feedback) technologies.

Pre-enrollment Reference Information

Practice Environment

Since this is a theory-focused lecture, no separate practice environment is required.
[Added Content] However, if you want to practice on your own with the content from the additional practical lessons, you can prepare Google Colab. Google Colab can be used for free immediately if you have a Google account (however, in special cases among the practice content, server performance provided only in paid plans may be required).

Learning Materials

I will attach the lecture materials in PDF file format.

Prerequisites and Important Notes

Having background knowledge in natural language processing, artificial intelligence, deep learning, and reinforcement learning will help you better understand the content.
[Added Content] If you want to practice on your own with the content from the additional practical lessons, it would be very helpful to know Python programming and machine learning/deep learning programming.

Recommended for
these people

Who is this course right for?

Someone wishing to learn LLM principles with a theoretical focus.
Those who want to understand the LLM creation process

Need to know before starting?

Deep Learning
Reinforcement Learning
Natural Language Processing

Hello
This is

528

Learners

Reviews

Answers

4.5

Rating

Courses

IT가 취미이자 직업인 사람입니다.

다양한 저술, 번역, 자문, 개발, 강의 경력이 있습니다.

Curriculum

All

196 lectures ∙ (32hr 31min)

Course Materials:

Lecture resources

Section 1. [Basic] Understanding Generative Artificial Intelligence

2 lectures ∙ (34min)

Section 2. [Advanced] 'Understanding Generative Artificial Intelligence' Advanced

3 lectures ∙ (1hr 27min)

Section 3. [Practice] 'Understanding Generative Artificial Intelligence' Practice

3 lectures ∙ (2hr 26min)

Section 4. [Basic] Deep Understanding of Language Model Principles

4 lectures ∙ (1hr 35min)

Section 5. [Practice] 'Deep Understanding of Language Model Principles' Practice

3 lectures ∙ (2hr 8min)

Section 6. [Basic] Autoregressive Model vs. Self-Attention Model

6 lectures ∙ (1hr 20min)

Section 7. [Advanced] 'Autoregressive Models vs. Self-Attention Models' Advanced

5 lectures ∙ (2hr 40min)

Section 8. [Practice] 'Autoregressive Model vs. Self-Attention Model' Practice

3 lectures ∙ (3hr 13min)

Section 9. [Advanced] Transformer Architecture

5 lectures ∙ (1hr 20min)

Section 10. [Practice] 'Transformer Architecture' Practice

14 lectures

Section 11. [Advanced] Comparison of Open-Source LLM and Closed-Source LLM

6 lectures ∙ (44min)

Section 12. [Practice] 'Comparison of Open-Source LLM and Closed-Source LLM' Practice

3 lectures ∙ (1hr 18min)

Section 13. [Basic] Understanding Fundamental Concepts Related to LLM Training

3 lectures ∙ (1hr 24min)

Section 14. 'Understanding Basic Concepts Related to LLM Training' Advanced

3 lectures ∙ (1hr 53min)

Section 15. (Publishing in progress) [Hands-on] 'Understanding Basic Concepts Related to LLM Training' Hands-on

3 lectures ∙ (44min)

Section 16. (Publishing in Progress) [Basic] Understanding the Complete Process of LLM Creation

12 lectures ∙ (3hr 55min)

Section 17. [Advanced] 'Understanding the Complete LLM Development Process' Advanced

22 lectures

Section 18. [Practice] 'Understanding the Complete LLM Development Process' Practice (Python + Google Colab)

23 lectures

Section 19. [Basic] Detailed LLM Training Process

6 lectures ∙ (1hr 35min)

Section 20. [Advanced] 'Detailed LLM Training Process' Advanced

3 lectures

Section 21. [Practice] 'LLM Training Process Details' Practice

3 lectures

Section 22. [Basic] LLM Evaluation Methods

4 lectures ∙ (1hr 18min)

Section 23. [Advanced] 'LLM Evaluation Methods' Advanced

3 lectures

Section 24. [Practice] 'LLM Evaluation Methods' Practice

3 lectures

Section 25. [Basic] LLM Lightweight

5 lectures ∙ (2hr 21min)

Section 26. [Advanced] 'LLM Optimization' Advanced

3 lectures

Section 27. [Practice] 'LLM Optimization' Practice

4 lectures

Section 28. [Advanced] Ethical Considerations and Responsible AI for LLMs

3 lectures

Section 29. [Practice] 'Ethical Considerations of LLM and Responsible AI' Practice

3 lectures

Section 30. [Advanced] LLM Applications and Deployment Strategies

3 lectures

Section 31. [Practice] 'LLM Application and Deployment Strategy' Practice

3 lectures

Section 32. [Advanced] Latest LLM Technology Trends

4 lectures ∙ (27min)

Section 33. [Practice] 'Latest LLM Technology Trends' Practice

3 lectures

Section 34. [Advanced] Latest LLM Research Trends and Future Prospects

4 lectures

Section 35. [Practice] 'Latest LLM Research Trends and Future Prospects' Practice

4 lectures

Section 36. [Advanced] Practical Applications of Generative Artificial Intelligence

3 lectures

Section 37. [Practice] 'Practical Application of Generative AI' Practice

3 lectures

Section 38. [Advanced] Guide to Small-Scale Practice and Project-Based Learning Methods

3 lectures

Section 39. [Practice] 'Small-scale Practice and Project-based Learning Method Guide' Practice

3 lectures

Published:

Last updated:

Reviews

All

2 reviews

3.5

2 reviews

khkwon
Reviews 3
∙
Average Rating 4.7
5
61% enrolled
- arigaram
  Instructor
  Thank you.
dbdusgur95
Reviews 1
∙
Average Rating 2.0
Edited
2
100% enrolled
.
- arigaram
  Instructor
  Thank you.

Limited time deal ends in 5 days

$58.30

24%

$77.00

arigaram's other courses

Check out other courses by the instructor!

Prompt Patterns for Developers (Vibe Coding)

arigaram

Now is the era of development using AI. To create better, more accurate code and documents by better utilizing AI, methods are needed, so we propose suitable ones.

초급

prompt engineering

Prompt Patterns for Developers (Vibe Coding)

arigaram

Breaking Down Secure Coding

arigaram

This lecture is designed for understanding even if you have no prior knowledge of what Secure Coding (Security Coding) is. After covering basic security coding concepts, it focuses on web service security. This was a special lecture presented under the auspices of OO University. It presents over 160 source codes with rich comments, pinpointing core topics and techniques immediately applicable in practice.

초급

security training, Penetration Testing, security

Breaking Down Secure Coding

arigaram

(For Product Managers) Fundamentals of LLM and Understanding LLM-Based Service Planning

arigaram

Describes the need for LLM, its technical background, and basic concepts.

입문

NLP, gpt, AI

(For Product Managers) Fundamentals of LLM and Understanding LLM-Based Service Planning

arigaram

Clean Coding: Learning Good Code Writing Techniques Through Cooking Analogies

arigaram

👨‍🍳 Like cooking code, neatly and deliciously! 『Clean Coding』 is a code cooking class for chef-like developers, where they joyfully learn clean coding through a cooking analogy. 🍽️

초급

Team Collaboration Tool, Coding Test, Refactoring

Clean Coding: Learning Good Code Writing Techniques Through Cooking Analogies

arigaram

Problem Statement: Basic Capabilities for Reducing 'Development Waste'

arigaram

Problematization (문제화) is a term also translated as problem posing, problem framing, problem setting, or problem definition. This concept involves questioning known facts (like requests or common sense) from a new perspective, defining the problem, and devising methods to solve it. Problematization should be the starting point for all development, yet it remains insufficiently discussed within the field. Performing projects or developing programs is, in essence, planning to solve problems. This means they are related to problematization. To solve a problem, it must first be clearly defined. However, problems are mostly presented as vague requests. Therefore, the ability to transform vague requests into clear problems is essential to reduce unnecessary 'development waste,' facilitate smooth collaboration, and accurately grasp users' true needs. This lecture helps train 'the thinking of problem construction' through practical examples and tools.

입문

Team Collaboration Tool, soft skills, Business Problem Solving

Problem Statement: Basic Capabilities for Reducing 'Development Waste'

arigaram

Cognitive Load Management Technology Breaking Through the Limits of RAG Performance

arigaram

What should be done when building a generative AI or LLM-based RAG (Retrieval-Augmented Generation) system, but the desired performance isn't achieved and there's no suitable solution? This lecture presents methods to improve RAG performance based on Cognitive Load theory. Through this lecture, you will understand the limitations of LLM context windows and learn how to effectively manage cognitive load in RAG systems. It is a practical-level theoretical lecture covering Chunk size and structure design, high-quality Chunk generation techniques, dynamic optimization, performance evaluation, and practical techniques.

중급이상

AI, ChatGPT, LLM

Cognitive Load Management Technology Breaking Through the Limits of RAG Performance

arigaram

AI-Based Full-Stack Development Practical Track for Beginners (Junior Level) - Master in One Day

arigaram

This enables junior developers to understand the entire development process from planning to code writing, deployment, and validation by using artificial intelligence as a development tool, allowing them to survive and thrive in the AI era by acquiring the full-spectrum capabilities needed.

초급

Prototyping, crud

AI-Based Full-Stack Development Practical Track for Beginners (Junior Level) - Master in One Day

arigaram

Complete Mastery of Prompt Engineering

arigaram

This course is a systematic learning process for prompt engineering, which is a core technology for effectively utilizing large language models (LLMs) or generative artificial intelligence. It covers a wide range of topics from basic theory to practical techniques, as well as the latest application cases and security/ethical issues, providing practical help to LLM-based service developers, data scientists, and AI planners alike.

중급이상

prompt engineering

Complete Mastery of Prompt Engineering

arigaram

Creating an AI Judge with CrewAI and LLM

arigaram

We create an AI judge by combining CrewAI, facilitating LLM-based multi-agent technology, with knowledge graphs and inference systems.

중급이상

AI, LLM, AI Agent

Creating an AI Judge with CrewAI and LLM

arigaram

(For Planners) Service Design for LLM Applications

arigaram

You can learn the concepts and methods of 'service design' necessary when planning applications or web services based on LLMs, a type of generative artificial intelligence (generative AI).

입문

LLM, Generative AI

(For Planners) Service Design for LLM Applications

arigaram

History and Development of LLMs

arigaram

Describes in detail the various language models developed from the inception of Natural Language Processing technology to the process leading up to the latest LLM models.

입문

NLP, RNN, self-attention

History and Development of LLMs

arigaram

(For Planners) Limitations and Future Prospects of LLMs

arigaram

We explore the limitations of LLMs, methods to overcome them, and the latest research topics.

입문

NLP, Service Planning, Content Planning

(For Planners) Limitations and Future Prospects of LLMs

arigaram

(For Planners) LLM Application Service Case Analysis

arigaram

Let's explore the types of applications or web services that can be built using LLMs.

입문

NLP, Service Planning, Content Planning

(For Planners) LLM Application Service Case Analysis

arigaram

Java Naming

arigaram

"One name change, code transformed." "The key to readable code, 'naming for readability,' summarized from principles to examples." "Easy-to-read names begin collaboration."

초급

naming-conventions, naming, renaming

Java Naming

arigaram

A Quick Overview of C Language

arigaram

You can quickly understand C language's basic concepts and grammar.

입문

C, Embedded

A Quick Overview of C Language

arigaram

(For Planners) Understanding Methods for Collecting and Analyzing User Requirements for LLM Applications

arigaram

We will examine methods for collecting and analyzing user requirements to plan LLM services.

입문

Project Management (PM), Service Planning, AI

(For Planners) Understanding Methods for Collecting and Analyzing User Requirements for LLM Applications

arigaram

Similar courses

Explore other courses in the same field!

First Time Creating Custom LLMs – LoRA & QLoRA Fine-tuning Introduction

HappyAI

"LoRA-based lightweight fine-tuning: Your first step to creating a customized LLM!" This course is an introductory hands-on lecture designed so that even those encountering LLMs for the first time can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading models → applying data → training → comparing results. In a short time, you'll directly experience the flow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, gaining an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

입문

Deep Learning(DL), NLP, AI

First Time Creating Custom LLMs – LoRA & QLoRA Fine-tuning Introduction

HappyAI

Large Language Models, Just the Essentials!

haesunpark

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

입문

Artificial Neural Network, PyTorch, LLM

Large Language Models, Just the Essentials!

haesunpark

(For Product Managers) Fundamentals of LLM and Understanding LLM-Based Service Planning

arigaram

Describes the need for LLM, its technical background, and basic concepts.

입문

NLP, gpt, AI

(For Product Managers) Fundamentals of LLM and Understanding LLM-Based Service Planning

arigaram

Chat GPT, DALL-E: Learning the Core Principles of Generative AI

skmns

Now is truly the era of Generative AI! Beyond just using the latest tech, gain the knowledge and experience to grasp its core principles and apply Generative AI to your daily life!

초급

openai, AI, ChatGPT

Chat GPT, DALL-E: Learning the Core Principles of Generative AI

skmns

History and Development of LLMs

arigaram

Describes in detail the various language models developed from the inception of Natural Language Processing technology to the process leading up to the latest LLM models.

입문

NLP, RNN, self-attention

History and Development of LLMs

arigaram

Time series data processing using Python and deep learning

YoungJea Oh

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

중급이상

AI, python3, Deep Learning(DL)

Time series data processing using Python and deep learning

YoungJea Oh

Creating Custom LLMs: From Basic RAG Concepts to Multimodal·Agent Practice for Beginners

HappyAI

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

입문

Python, vector-database, LLM

Creating Custom LLMs: From Basic RAG Concepts to Multimodal·Agent Practice for Beginners

HappyAI

90-Minute Complete Guide: From LLM Agent Basics to Practice – Learning AI Agents Through Hands-on Experience

HappyAI

The era of AI simply providing answers is over. Now is the age of LLM Agents that make decisions and take actions on their own. This course is an introductory lecture where you learn the core principles and structure of agents by implementing them directly through just 90 minutes of hands-on practice. We minimize complex theory and focus on code-centered practical flow, allowing you to directly experience "how AI makes decisions and uses tools." Beyond prompt engineering, let's take the first step into AI automation together.

초급

multi-agent, LLM, LangChain

90-Minute Complete Guide: From LLM Agent Basics to Practice – Learning AI Agents Through Hands-on Experience

HappyAI

Edge Computing Fundamentals and Applications: From Concepts to Practical Implementation Strategies

sdj0831

In an era of explosive data growth and crucial real-time processing, edge computing has established itself as an essential technology, no longer an option. This course is designed to systematically teach everything, from the core concepts of edge computing to deployment strategies for its application in real business environments. Through this course, students will firmly solidify the fundamental principles of edge computing and acquire practical knowledge and know-how applicable to real-world tasks and projects. Take the most certain first step towards becoming an edge computing expert, a core technology in the future IT environment!

입문

AWS, Google Cloud Platform, CI/CD

Edge Computing Fundamentals and Applications: From Concepts to Practical Implementation Strategies

sdj0831

Understanding PyTorch at a Glance with Code_Project Edition

onepm

The PyTorch_Project Edition, understood instantly with code, implements practical deep learning projects using image, video, and NLP data, understanding model structure and flow through code-centric learning over theory.

중급이상

Deep Learning(DL), AI

Understanding PyTorch at a Glance with Code_Project Edition

onepm

AI Introduction Study Method by a Graduate Student Who Wrote 2 Papers as an Undergraduate

epoch

Introducing AI entry-level study methods for undergraduates.

입문

AI, Linear Algebra, Probability and Statistics

AI Introduction Study Method by a Graduate Student Who Wrote 2 Papers as an Undergraduate

epoch

Generative AI Utilization Methods for Businessmen

veron48

In the midst of daily overwhelming tasks and endless repetitive work, you've probably wondered, "Isn't there a smarter and more efficient way to do this?" Haven't you felt frustrated wasting time on drafting reports, repeatedly failing to find new insights in brainstorming meetings, and struggling to keep up with competitors' pace? Right now, the most powerful tool to solve those concerns has emerged. It's Generative AI. This isn't just a trending technology. It's the most fundamental productivity revolution of our time, and those who first capture and utilize this change will become the winners of future business. This course will teach you how to immediately apply Generative AI, which will become your most capable assistant on your desk, to your actual work. Let's set aside complex theories for now. We focus on **"So what can I do with this technology starting tomorrow?"** 💡 We'll help you reclaim your time and creativity. Our core goal is your **'work automation'** and **'creativity maximization'**. 90% reduction in report writing time: Learn how to delegate the creation of reports, proposals, and email drafts that consume your precious time every week to AI. Simply input key data and direction, and AI will immediately complete professional documents for you. You can now focus on being a strategic thinker and leader, not a document writer. Ideas that outpace competitors: Need new marketing campaigns, innovative product designs, or content ideas that captivate customers? Practice using AI as a partner to instantly generate dozens of creative alternatives and predict market reactions. Data-driven smart decision making: Don't get lost in countless materials. AI quickly summarizes vast amounts of data and provides key insights, helping you make strategic judgments based on data rather than intuition. 🛠️ 'Prompts' are the new language. Utilizing Generative AI well ultimately means being good at **'prompt engineering'**. Like commanding a capable employee, the skill of giving clear and specific instructions to AI determines success or failure. In this course, we'll teach you the core formula for **"how to ask questions to get the desired results."** You'll directly write customized prompts optimized for your role - whether you're a marketer, developer, or planner - and experience how dramatically different the results can be. This knowledge will become a core future competency that won't waver no matter what new AI tools emerge. 🏆 Through this course, you'll become a 'future businessman'. Generative AI is no longer a 'choice' but a 'necessity'. If you reject this technology, your competitors will work 10 times faster every day. But if you seize this opportunity, you can be reborn as a 'future-ready talent' who maximizes productivity, leads creative innovation, and dramatically boosts business performance. Don't fear Generative AI. What matters is knowing how to utilize it well. Start your work innovation journey through this course. It will be the one decision that explodes your business potential. Register right now and experience a new way of working where you work less and achieve more.

입문

Business Productivity, AI, ChatGPT

Generative AI Utilization Methods for Businessmen

veron48

From LDM to DiT, Complete Mastery of Diffusion Through Implementation II

Sotaaz

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

초급

Python, Deep Learning(DL), Stable Diffusion

From LDM to DiT, Complete Mastery of Diffusion Through Implementation II

Sotaaz

High-Quality AI Video All-in-One Class: Midjourney & Runway (25+a Scene Production)

Dong Ah K

Create high-quality AI videos with Midjourney&Runway. Director Kim Dong-ah, who collaborated with Samsung, will personally guide you step-by-step through the AI video production process based on a virtual brand.

중급이상

Video Production, AI, Midjourney

High-Quality AI Video All-in-One Class: Midjourney & Runway (25+a Scene Production)

Dong Ah K

(For Planners) LLM Application Service Case Analysis

arigaram

Let's explore the types of applications or web services that can be built using LLMs.

입문

NLP, Service Planning, Content Planning

(For Planners) LLM Application Service Case Analysis

arigaram

Preparing for AI Graduate School A-Z: From Spec Preparation to Contacting Professors, Application Documents, and Interviews

epoch

Through this lecture, you'll be able to learn about the AI graduate school admission process and preparation methods from start to finish! I'll also share some great tips to increase your chances of getting accepted!

입문

AI, Interview

Preparing for AI Graduate School A-Z: From Spec Preparation to Contacting Professors, Application Documents, and Interviews

epoch

DDPM to DDIM, Complete Mastery of Diffusion Through Implementation I

Sotaaz

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

초급

Python, Deep Learning(DL), AI

DDPM to DDIM, Complete Mastery of Diffusion Through Implementation I

Sotaaz

Artificial Intelligence (AI) - Learning Runway AI that Creates New Videos and Moving Images

usefulit

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

입문

AI, Generative AI, Deep Learning(DL)

Artificial Intelligence (AI) - Learning Runway AI that Creates New Videos and Moving Images

usefulit

Anyone Can Do It! Create Your Own Service Without Coding with 'VibeCoding'

selfishclub

Now is an era where anyone can create services without coding. Selfish Club crew members implemented services for actual use with only the essential features needed within 3 weeks. These are cases where crew members who don't know how to code directly created services to solve problems using only natural language prompts. Vibe coding is no longer an unfamiliar concept! We're sharing real cases of 'what actual problems can be solved through coding' with everyone through actual examples!

입문

Vibe Coding, replit, ChatGPT

Anyone Can Do It! Create Your Own Service Without Coding with 'VibeCoding'

selfishclub

Let's Efficiently Manipulate AI! ChatGPT Prompt Engineering Part. 1

usefulit

Learn the core techniques of 'Prompt Engineering' to get exactly the answers you want from AI. From a complete beginner's first steps to expert practical applications, maximize your AI utilization skills!

입문

ChatGPT, prompt engineering, Generative AI

Let's Efficiently Manipulate AI! ChatGPT Prompt Engineering Part. 1

usefulit

Understanding the Fundamental Principles of Large Language Models (LLMs)

What you will learn!

🧭Precautions

🧭Change History