Building OCR that actually works in real-world scenarios, here's how to do it.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

By the end of this course, you will be able to implement Apache Spark projects in your organization.

Learn how to use OpenCV and process images through a practical implementation of business card recognition. OpenCV is briefly explained, and the OpenCV functions required to implement business card recognition are explained in more detail. In other words, rather than a basic course, it provides an essence for the practical application of business card recognition. It also includes a method of utilizing Tesseract and Microsoft's OCR engine. The source code of this lecture is based on Python, but since you will learn the OpenCV classes and functions used and the image recognition and processing technology, it will not be difficult to apply it to Android and iOS based on this. Those who take the course will be provided with lecture slides and actual business card recognition source code. The lecture contents were further improved and verified through three offline seminars in January and February 2017, and the instructor has carefully recorded them again for online use. These are the OpenCV functions used in this lecture. cv2.Canny() cv2.GaussianBlur() cv2.VideoCapture() cv2.adaptiveThreshold() cv2.approxPolyDP() cv2.arcLength() cv2.boundingRect() cv2.boxPoints() cv2.contourArea() cv2.convertScaleAbs() cv2.createTrackbar() cv2.cvtColor() cv2.destroyAllWindows() cv2.dilate() cv2.drawContour s() cv2.findContours() cv2.getAffineTransform() cv2.getPerspectiveTransform() cv2.getTrackbarPos() cv2.imread() cv2.imshow() cv2.imwrite() cv2.minAreaRect() cv2.namedWindow() cv2.rectangle() cv2.resize() cv2.threshold() cv2.waitKey() cv2.warpAffine() cv2.warpPerspective()

OpenAI Practical Application Course, where you directly implement various AI functions such as text, image, voice, translation, and content review in a Java environment.

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.

From server setup to blog and YouTube automation, build a complete AI workflow with n8n. Practice setting up a stable operating environment including Contabo VPS-based server installation, Ubuntu, Docker, and Portainer environment setup, domain connection, and HTTPS security configuration. Cover FastAPI and Docker-based search API and file server development, Airtable and n8n integration, blog AI agent construction, and image and video automation. Complete profitable automation strategies that can be directly applied to actual service operations.

During the Gold Rush, it was said that it was easier to become rich by selling gold mining tools than by mining gold yourself. How about selling programs in the ChatGPT Age of Exploration?

Chatgpt is a program that reads and comments on posts written by my neighbors Lesson on developing marketing automation and neighbor management programs

No more object detection! Learn the latest trends in pose estimation, the latest methods for estimating poses from images and videos, and learn deep learning techniques to train and utilize 2D pose estimation models from your own videos.

This course is a step-by-step learning process that covers the principles of image and object recognition using deep learning, from fundamentals to the latest models. - Building Foundations: Understanding tensors and basic neural network structures with PyTorch - Understanding Images: Learning computer vision concepts, image data structures, and augmentation techniques - CNN Model Training: Hands-on image classification practice with Convolutional Neural Networks (CNN) using datasets like CIFAR-10 - Transfer Learning: Leveraging pre-trained models to achieve fast learning with limited data - Object Detection: Understanding and practicing with the latest object detection models including R-CNN, YOLO, SSD, DETR - Segmentation: Experiencing pixel-level object segmentation through U-Net and Mask R-CNN

This is an all-in-one lecture where you can learn the entire process from the basics of deep learning/TensorFlow/computer vision to practical applications through a practical vehicle license plate recognition project. Through various practical exercises, you can develop practical skills to apply the latest deep learning models to custom datasets.

This is a course that beginners can follow along with in a fun and easy way. Beyond simple chatbots, AI Agents that automate industry-specific business workflows. This course quickly covers the basic structure and core technologies of AI Agents (LangChain, LangGraph, RAG) in 1.5 hours, and is an introductory course where you build practical skills by directly creating mini agents that work with actual code. After completing the course, you'll understand the necessity of industry-specific data preprocessing and scalable design, and be prepared for advanced concepts covered in deeper courses.

Create a program using the GPT API and even generate images and videos!

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!

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

Is AI difficult because of complex algorithms? Actually, the real power of AI comes from 'data'. You need to understand the essence of data to properly handle AI. This is a lecture that explains the roots of AI from a data perspective, covering everything from basic philosophy to perceptrons, CNNs, and data processing and ethics.

Even non-majors can easily complete this 【Data Analysis Customized Basic Python】 course! Learn the basics of ChatGPT utilization as well, and create a 『starting point』 for data analysis and AI utilization. From essential prerequisite courses for data analysis to customized learning from a 『Data Analysis Instructor』, you can build deep foundational skills while learning quickly, instill the courage and confidence needed for coding learning, and even provide future data analysis learning directions.

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.

Learn how to build a real-time fire detection system using Next.js, YOLOv11, and FastAPI.

AI writes on my Slack and Notion for me? Work smarter with MCP that surpasses the limitations of existing AI!

This course focuses on developing practical visualization skills that can be immediately applied in the workplace by utilizing Seaborn, a core tool for data visualization, and plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can enhance both your ability to create insight-driven visual materials and implement web-based dashboards.