[Practical AIoT] Perfect Preparation for Smart Mirror Makerthon: LLM, CV, and Hardware Design

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.

What if AI could directly open a browser and write shell scripts to resolve server issues? Through practical automation know-how implemented with OpenClaw across Windows and Linux, we guide you on the shortest path to completing your own powerful remote development environment without complex configurations.

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!

I will explain what embedded development is. I will provide a clear explanation of the concept without any vague or abstract descriptions.

Do you still code manually these days? We are now in an era where even "vibe coding" feels like a chore! Become an AI automation expert with n8n, no coding required. A guide to designing automation workflows that can be applied immediately to real-world tasks. Practical automation strategies to maximize work efficiency. Build intuitive workflows with n8n and experience a revolution in the way you work.

Complete Understanding of Robotics High- and Low-Level Controllers, Sensors, and Ultra-High-Speed Communication Concepts Through the Unitree Go2 Example

A comprehensive course covering AI-era-proof Silicon Valley internships, 30 big-tech system designs, and hands-on open-source contributions. Using real-world cases from Netflix, Tesla, Meta, Tinder, and other big tech companies, you’ll design and scale 30 practical system designs. Finally, you’ll design your own service from architecture to database to scaling.

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

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.

This is a practical course on 'AI-based Circuit Design/Verification Automation' as required by professionals at Samsung Electronics and SK Hynix. Master the skills for LDO IP design and verification automation based on TSMC 180nm PDK, AI-based AXI-Lite RTL implementation, and Python/TCL/Batch script regression automation.

How does the architecture change when an LLM is integrated into an existing backend? Going beyond simple LLM API calls, this introductory system design course helps you understand the core components of AI services—such as Model Routers, LLM Gateways, and Vector DBs—and progressively design operational AI service architectures, including RAG and AI Agents. It helps those who want to build their own LLM-based AI services quickly learn the design process.

This course covers the entire process of evolving ChatGPT from a simple conversational AI into an intelligent AI Agent that performs real-world tasks. By utilizing the three core frameworks—LangChain, LangGraph, and LangSmith—you will build AI Agents that can be used immediately in practice.

Linear Algebra in One Go. Learn linear algebra concepts in detail—concepts that, once learned, will serve as the building blocks for your thinking for a lifetime. Going beyond simple explanations, we kindly guide you through practical exercises to show you how to apply them in real-world situations.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

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.

Innovation in Work Efficiency and Creativity – Agentic AI AI is now evolving beyond simple automation into a new stage where it can judge and execute on its own. Agentic AI is at the center of this change, serving as a core technology that helps businesses and organizations operate smarter. In this course, we have organized the content clearly and simply so you can understand how Agentic AI connects decision-making with execution and how it can be applied to practical work. Discover the essential knowledge for every professional preparing for the future right now.

Hi Guys. Welcome to my Full AI Course 2026: ChatGPT, Google Gemini, Midjourney, Firefly. In my course you will learn everything about all AI Tools. I have created video lessons on every feature of these AI tools. You will get to see the real practical implementation of how to use every feature of this tools.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

As you study CPUs and RISC-V, you may understand what the instructions do but still find yourself asking, “How are these instructions actually executed in hardware?” and “How can I verify that a CPU I designed myself really works correctly?” Many existing resources stop at ISA explanations or simple CPU examples, and many others rely on completed IP, making it difficult to experience the entire process of designing and verifying a CPU yourself and running programs on an actual FPGA. I have spent many years working in R&D and business in the system semiconductor field, and I have observed this learning gap firsthand while teaching semiconductor design and FPGA at a university. For this reason, rather than providing a completed CPU and simply checking that it operates, this course is structured around designing the CPU directly in a White Box approach, starting with the question, “What hardware is required to execute RV32I instructions?” In Volume 3, we expand Volume 2’s TinyRV32I into a Full-Version 32-bit CPU that uses 40 RV32I instructions. We extend the datapath, Decoder, Branch·Jump, and Byte/Halfword Memory Access, and implement Forwarding and Load-Use Interlock so that the 5-stage Pipeline can handle data dependencies on its own. We then integrate CSR, Exception, Interrupt, Trap, and MRET to complete a CPU capable of handling not only normal program execution but also exceptions and external events. The design does not end with RTL Simulation. You will run actual programs on the CPU, compare the results with the Spike Reference Model, and learn the purpose of the RISC-V Architecture Compatibility Test as well as how to perform and evaluate it through real verification cases. Finally, you will implement the verified CPU on an Arty S7-25 FPGA, receive input from Switches and Buttons, run programs, and confirm the results by displaying them on LEDs. The problem this course aims to solve is not simply learning more RISC-V instructions. Its goal is to connect instructions → CPU architecture → Verilog RTL → Pipeline control → program execution → standards-based verification → actual FPGA operation into a single flow, enabling students to fundamentally understand the internal structure of a CPU and develop the engineering skills to design and verify one themselves.

Do the results of vibe coding keep turning out differently from what you intended? Learn a workflow that uses specification-driven development (SDD) and Spec Kit to define goals, scope, business rules, and completion criteria, then shares the same specifications with Claude Code and Codex to reduce repetitive revisions.