[AI Fundamentals] Understanding CNNs for AI Research Engineers

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!

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 will help you become a deep learning-based computer vision expert who is needed in the field by providing a deep theoretical explanation of object detection and segmentation and practical examples that can be used immediately in the field.

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.

This newly designed course, drawing from the instructor's initial deep learning failures, helps you progressively learn deep learning essentials: math, theory, PyTorch-based implementation, transfer learning, and GPT's core Transformer.

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

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.

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

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.

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

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.