[Complete NLP Mastery I] The Birth of Attention: Understanding NLP from RNN·Seq2Seq Limitations to Implementing Attention

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.

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.

This course is not just about "how to implement" a Transformer, but about dissecting why this architecture was created, what role each module plays, and how the entire model works from the designer's perspective. We deeply analyze the internal computation principles of Self-Attention and Multi-Head Attention, and directly verify through formulas, papers, and implementation code what limitations Positional Encoding, Feed-Forward Networks, and Encoder·Decoder structures were introduced to solve. Starting from Attention, we assemble the entire Transformer structure ourselves, and actually perform training to experience firsthand how the model operates. This course is the most structured and practical roadmap for "anyone who wants to completely understand Transformers."

We implement the latest Transformer-based PixArt and lightweight adaptation SANA step by step from theory to code. Building on DDPM·DDIM·LDM·DiT covered in Parts I·II, we complete hands-on practice including text encoder integration, samplers (DDIM/ODE), v-prediction/CFG tuning, and small-scale data style fine-tuning.

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.

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

Conquer everything about world-changing AI in just 4 lectures! Lecture 1. The True Identity of Artificial Intelligence AI is not magic. It's a digital brain created by analyzing human 'intelligence' like a mathematical formula! We thoroughly dissect the core structure of seemingly smart artificial intelligence. Lecture 2. Algorithm, AI's Creative (?) Thinking Method How does a pondering AI find answers? Experience firsthand the strategies of AI that plays Go, finds routes, and predicts your search results. Lecture 3. AI Hardware, The Engine That Turns Imagination Into Reality! The chip inside your smartphone is the heart of AI. We dissect everything from CPU, GPU, TPU, to even those unknown names. Lecture 4. The Great Clash of Machine Learning and Deep Learning! What on earth does it mean for AI to 'learn'? From classic rule-based AI to the currently hot ChatGPT! We trace the evolution of learning.

This course is not just about "how to implement" a Transformer, but about dissecting why this architecture was created, what role each module plays, and how the entire model works from the designer's perspective. We deeply analyze the internal computation principles of Self-Attention and Multi-Head Attention, and directly verify through formulas, papers, and implementation code what limitations Positional Encoding, Feed-Forward Networks, and Encoder·Decoder structures were introduced to solve. Starting from Attention, we assemble the entire Transformer structure ourselves, and actually perform training to experience firsthand how the model operates. This course is the most structured and practical roadmap for "anyone who wants to completely understand Transformers."

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.

From Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder based MarianMT translation model, and even Vision Transformer, you'll learn Transformer inside and out by implementing them directly in code.

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.

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

Is this kind of introduction even important right now? Use the DeepSeek R1 model right away.

Why this course is special: Key Advantages • Intuitive Visualization: Directly observe algorithm operations in real-time with Pygame 2D simulations • Practical Implementation Experience: Go beyond theory and internalize autonomous driving algorithms by coding directly • Master Core Algorithms: Focused learning of essential algorithms such as Dijkstra, Pure Pursuit, ICP, etc. • Step-by-step Advanced Learning: Systematic difficulty progression from basics to SLAM • Lidar-based SLAM: Practical map building and localization in unknown environments

Analyze over 40 types of economic indicators and stock data with AI to create your own powerful stock price analysis model that predicts not only S&P 500 and QQQ ETF, but also individual stocks!

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python

A general education lecture for the public to shed the vague fear of artificial intelligence. Through concrete examples, we learn what AI learning truly means. Also, via specific examples, we examine AI's 'brain structure' and how it thinks.

What to Expect from This Course Welcome to LangGraph in Action, your ultimate guide to mastering the design and deployment of advanced AI agents using LangGraph. In this course, you’ll explore the fundamentals of building modular, scalable, and production-ready agents, all with a hands-on approach. From understanding the basics of LangGraph’s state-based design to creating a full-stack application, you’ll gain the skills needed to bring AI agents to life. Course Highlights State-Based Design: Dive into LangGraph’s core philosophy of nodes and edges to create structured, maintainable agents. Memory Management: Explore short-term memory with checkpointers and long-term memory with the Store object to enable agents that adapt and learn. Advanced Workflows: Build human-in-the-loop systems, implement parallel execution, and master multi-agent patterns. Production-Ready Development: Learn asynchronous operations, subgraphs, and create full-stack applications using FastAPI and Docker. By the end of the course, you’ll not only have a strong theoretical understanding but also the practical skills to deploy AI agents anywhere, entirely with open-source tools. Whether you're a developer aiming to stay ahead of the curve or a seasoned engineer looking to expand your AI toolkit, this course equips you for the rapidly growing field of AI agents. With the increasing adoption of AI agents in real-world applications, this course ensures you're prepared to design, build, and deploy advanced systems that solve practical challenges. Let’s start building and shaping the future of AI together!

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

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.

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

This course starts with the question "If I had been on the Titanic, could I have survived?" and completes a full-stack project that develops an AI model to predict survival probability based on actual data and serves it as a web service. You'll practice the entire process of AI and web development, from deep learning modeling using PyTorch, building a backend server with FastAPI, to implementing a user interface with Next.js.