From the concept of the latest deep learning technology Vision Transformer to Pytorch implementation

This is a lecture on implementing various artificial neural networks using PyTorch, a deep learning framework with very high utility in the field of artificial intelligence.

This course provides essential knowledge needed to understand the structure and operating principles of various artificial neural networks and create good models.

This is a lecture that studies the physical information neural network, one of the next-generation innovative technologies of deep learning, and implements it directly using Pytorch. Let's learn the next-generation innovative technology of artificial intelligence with me!

This course will teach you from the early YOLO model, a real-time object recognition model, to the latest model. In addition, you will learn various deep learning techniques along with object recognition.

Learn how to understand advanced machine learning models without math and implement them easily in Python.

Covers essential linear algebra for Machine Learning/Deep Learning research.

This course is a fun project that learns theories and processes images and videos with OpenCV (Open Source Computer Vision Library) and uses deep learning modules (dnn, Deep Neural Network) to identify and recognize faces, eyes, and various objects.

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.

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

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 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

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 an introductory course to understand the concepts and application methods of Vision-Language Models (VLM), and to practically run the LLaVA model in an Ollama-based environment, practicing the process of integrating it with MCP (Model Context Protocol). This course covers the principles of multimodal models, Quantization, service, and integrated demo development, providing a balanced mix of theory and practice.

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 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.

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.

This is a lecture to build an AI portfolio by doing a clone project of airbnb data science team's Amenity Detection project, an excellent TensorFlow utilization case, and organizing the experience on a GitHub blog.

Let's create a web app that uploads an image and uses OpenCV to process it and detect faces.

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.