[AI Practice] Understanding Diffusion Models through Prompt-to-prompt Paper Implementation

When researching AI or conducting a project using it, basic paper implementation is essential. Let's upgrade our practical skills by implementing an actual paper through this lecture!

Learn deep learning through PyTorch. Ultimately, the course covers image classification.

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.

A course to truly make you a hands-on game developer. Push your limits with 4 hands-on projects and 3 AI-specialized games!

When you learn machine learning, you learn how to improve accuracy. But the moment you try to put that model onto an actual chip, questions no one has answered remain. How many multiplications does this model perform? How much does it need to remember, and for how long? Can the chip specifications you are targeting handle those requirements? Machine learning textbooks talk about accuracy, and semiconductor textbooks talk about circuits, but materials that bridge the gap between them are rare. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs. The model with the highest accuracy often turned out not to fit on the board. In this course, you will build six algorithms directly in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy each time. Then you will convert those figures into the resources of an actual FPGA board. Saying that something requires a certain number of multiplications is not enough to gauge its size, but knowing that it uses a certain percentage of the board makes the decision clearer. After nine Labs, when the six models are brought together in a single table, an unexpected picture emerges. We spent nine lessons counting multiplications, but multiplications were not what held us back. After that, we reduce floating-point values to integers, manage bit widths, and even establish criteria for evaluating the circuit. You do not need an FPGA board. The course is conducted entirely in Python, and the board is used only as a reference for converting the measurements.

From server setup to blog and YouTube automation, build a complete AI workflow with n8n. We will practice creating a stable operating environment, covering Contabo VPS-based server installation, Ubuntu/Docker/Portainer environment setup, domain connection, and HTTPS security configuration. The course covers Search API and file server development using FastAPI and Docker, Airtable and n8n integration, building blog AI agents, and image/video automation. You will be able to complete a profit-generating automation strategy that can be immediately applied to actual service operations.

"Will you make money just by building an app? No." Vibe Coding is merely a tool; the core is 'profitable planning and strategy.' This course goes beyond simple Vibe Coding technical training to reveal a practical manual for building a revenue automation system, learned firsthand by a non-developer operating 200 apps.

You don't need to know any coding at all. In just half a day of studying VIBE Coding, you'll be able to create programs without writing any code. Let's learn the latest AI coding methods by directly creating 10 step-by-step examples such as web scraping, TTS, chatbots, and games.

Hot image generation model! Let's understand it by implementing it from the basics! Let's implement AUTO ENCODER / VAE / GAN / DIFFUSION together!

Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

This is a process for completing IoT products without complex coding, using the Vibe Coding methodology powered by AI agents. You can experience the entire product development cycle in a condensed format—from embedded hardware control and AI vision recognition to PCB design, 3D printing, and web/app service development—allowing you to launch functional, commercial-grade IoT devices.

Theory, numerical approximations, intuition, and visualization in integral calculus. Enhance your math-coding skills.

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax—you will integrate nine blocks into a single chip and prove for yourself that "the Python reference and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."

In the era of smart factories and autonomous driving, is our production line truly safe? The remarkable advancement of Artificial Intelligence (AI) has brought innovative productivity and efficiency to the automotive manufacturing industry, but it is simultaneously creating unprecedented security threats to the Operational Technology (OT) environment within factories. This lecture breaks down "OT Security"—which can often feel vague and difficult—in a clear and easy-to-understand manner through vivid, real-world examples from the automotive industry.

Now your imagination becomes the ultimate game design document! With AI, you can have fun chatting as if you were building with LEGO and complete your first game. Try Google Gemini Pro for free!

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

Write meeting minutes, build apps, and even create videos—all on a single platform. Become an AI multiplayer with Google AI Studio!

Full-stack course from Figma design to AI-powered coding and deployment!! [Figma + Vibe Coding (HTML/CSS/Tailwind/React) + Git + Vercel Deployment] Your design exactly as it is!! Complete it with Vibe Coding without a single pixel of error! A full-stack guide to increasing practical productivity by 5x using AI! You too can become a full-stack designer capable of both design and coding. ✅ Implement your design intent without a single pixel of error ✅ Design architecture that AI understands ✅ Prompt engineering skills to precisely control AI ✅ Ability to skillfully fix code generated by AI Experience the amazing reduction of time from design to coding and deployment by 1/5.

It's time to move beyond the beginner level and step up to the standard of professional developers. We're going to reveal exactly how games are actually made in the industry.

Many people have learned FPGA and Verilog but get stuck at the question, "So, how do I actually build an AI accelerator?" This course resolves that curiosity. Using just a single entry-level FPGA and free tools, you will start from a hand-designed 3×3 kernel, train a CNN yourself, quantize the trained weights to INT8 to load onto the hardware, and verify that the results are Bit-True—not deviating by even a single bit from the Python model. As a professor with 30 years of experience in the semiconductor industry, I teach only verified content as 'working circuits' on actual FPGAs. By gaining the experience of designing and proving it yourself rather than just learning theory, you will acquire practical skills that are recognized in the fabless design and AI semiconductor fields.