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!

This course conducts hands-on practice related to Diffusion models among generative artificial intelligence models. By reading and implementing the prompt-to-prompt paper, which is a representative Diffusion model application paper, we expect to cultivate the ability to understand the latest artificial intelligence papers.

For those who have tried running hand-recognition examples but feel lost when it comes to teaching an AI your own hand gestures and turning them into something useful, I’ll guide you all the way from installation to data collection, training, and completing the web interface using just a webcam, drawing on my experience overseeing development and training vocational instructors.

Build a fully functional smart farm platform that integrates sensor data collection, MQTT communication, web dashboards, and AI-driven environmental analysis. Experience the entire process of agricultural digital transformation (DX) through a hands-on project, covering everything from IoT sensor integration and FastAPI/Spring-based API servers to React/Vue dashboard development and cloud deployment.

You will learn how to process images and videos with OpenCV, and how to identify and recognize faces and various objects using deep learning. This is a course where you will learn fun projects such as drowsiness prevention, age and gender identification, counting the number of people passing by, face tracking, and YOLO that finds bad apples and tangerines, along with theories.

This course teaches circuit/PCB design, STM32 firmware, and BLDC motor control all at once. You will design a three-phase inverter yourself and use it to drive an electric kick scooter.

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

This course provides a complete theoretical overview of the operations of convolutional and pooling layers, which are the core modules of Convolutional Neural Networks, and helps you develop implementation skills by building them yourself.

This is a linear algebra course that explores the meaning of vectors and matrices through illustrations and works through calculations using small examples. It covers the mathematics used in linear regression, PCA, SVD, tensors, and attention step by step. No programming knowledge is required.

Aim for a quick pass of the first nationally certified AI qualification! Includes three sets of practical problem-solving exercises reconstructed by the instructor after taking the exam personally, along with code templates.

Study the basics of AI hardware with Matbi. As a non-memory design expert, it contains essential knowledge for designing good AI HW.

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

Putting a neural network on an FPGA — it sounds familiar, but few people have actually done it. If you want to get ahead in capstone projects, competitions, or graduation projects, you need to go beyond training a model: you must be able to build and prove for yourself how its computations actually run inside a chip. This course helps you bridge that gap between theory and physical hardware. From reading the specifications to building a Python golden model, Machine Learning training, Verilog RTL design, and FPGA verification, you will complete a neural network accelerator (NPU) for recognizing MNIST digits written in real people’s handwriting from start to finish. From a Conv layer built 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 answer and the hardware output do not differ by even one bit (Bit-True)." The course also covers how to overcome that frustrating moment when the simulation runs perfectly, but the results are subtly different. We will walk through practical know-how that general deep learning textbooks do not cover, one step at a time — including integer quantization, array-axis ordering conversions, and preventing accumulator overflow. Drawing on the trial and error I have experienced through extensive work with RISC-V CPU design and FPGA Bit-True verification, I have built those lessons into this course so that you do not have to repeat them. By the end of the course, you will have a complete hardware project in hand and be able to say, "I designed it myself and proved it.&quot

From hardware control technologies genuinely required in real-world work to building custom build systems, with hands-on practice using industry-standard ST EV boards (Nucleo)

This course allows you to build an AI portfolio by cloning the Amenity Detection project from the Airbnb Data Science team—a prime example of TensorFlow utilization—and documenting the entire process on a GitHub blog.

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.

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.

Still lost on CNN even after studying it? I'll concisely explain CNN's core mechanics.

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!

--- Learning a technology means reclaiming the choices in your own life --- You may have connected an ESP32 development board and sensors before, but the process of developing a jumper-wire experiment into a complete device can feel daunting. There is a great deal of information online about circuit design, PCB fabrication, sensor control, and BLE and Wi-Fi communication. However, connecting this scattered information and completing a board that actually works requires a great deal of trial and error. In this course, you will design and build an ESP32-based sensor board yourself. You will configure the power and firmware download circuits, connect temperature and humidity and light sensors, design a PCB with KiCad, and order it for fabrication. Once the board is complete, you will upload firmware to it and transmit sensor data externally via BLE and Wi-Fi. By the end of the course, you will be able to go beyond simply assembling development boards and sensor modules: you will understand and be able to modify circuits and PCBs to suit your own purposes. The project completed in the course can be reused as a starting point for new projects such as environmental monitoring devices, smart home devices, educational kits, and IoT prototypes. Even in an era when generative AI can rapidly create digital content, physical information such as temperature, humidity, light, and movement in the real world can only be obtained through sensors and hardware. The ability to collect data with a device you built yourself and connect it to a network gives you the power to turn ideas into real-world systems. This course will be helpful for: * Those who have used Arduino or ESP32 development boards but are new to PCB design * Those who want to turn a sensor project made with jumper wires into a single complete board * Those who want to learn KiCad by following an actual ESP32 project * Those who want to build a device that collects sensor data and transmits it via BLE and Wi-Fi * Makers and educators developing environmental monitoring, smart home, or educational hardware * Aspiring entrepreneurs preparing their own IoT products or prototypes * Developers and content creators who want to analyze or process data collected from the real world or use it with AI models Move from being someone who uses development boards to someone who designs their own boards. This course does not merely teach you how to use sensors. It is a course where you experience the entire process of turning an idea into a circuit, turning the circuit into a PCB, and connecting the completed hardware to a network.