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Recap and Today's Theme Hello! In the previous episode, we covered pose estimation using OpenPose, a technique for detecting human joint positions and analyzing poses. OpenPose has applications in fields like sports analysis, rehabilitat...

Recap and Today's Theme Hello! In the previous episode, we introduced style transfer, a technique for applying the style of one image to another while preserving the content. This technology is gaining attention in the fields of art and ...

Recap and Today's Theme Hello! In the previous episode, we explored the fundamentals of facial recognition and its various applications in security and social media. Today, we will dive into pose estimation, a technique used to estimate ...

Recap and Today's Theme Hello! In the previous episode, we discussed image generation using GANs (Generative Adversarial Networks) and explored their applications in generating and transforming images. Today, we will focus on Style Trans...

Recap and Today's Theme Hello! In the previous episode, we explored the implementation of U-Net, focusing on its role as a segmentation model for medical images and its performance in fields like healthcare and autonomous driving. U-Net ...

Recap and Today's Theme Hello! In the previous episode, we discussed the workings and implementation of SSD (Single Shot MultiBox Detector), a real-time object detection model. SSD processes images in a single pass, detecting objects at ...

Recap and Today's Theme Hello! In the previous episode, we explored the fundamentals of segmentation and its applications in tasks that require pixel-level classification, such as medical imaging and autonomous driving. Today, we will di...

Recap and Today's Theme Hello! In the previous episode, we discussed YOLOv3 and how it performs real-time object detection by processing the entire image in a single pass. Today, we will introduce SSD (Single Shot MultiBox Detector), ano...

Recap and Today's Theme Hello! In the previous episode, we discussed the basics of object detection, covering popular techniques such as R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, each with its unique characteristics and approaches....

Recap and Today's Theme Hello! In the previous episode, we covered transfer learning, where we leveraged pre-trained models (like VGG16) and adapted them for new image classification tasks. Transfer learning allowed us to build high-accu...

Recap and Today's Theme Hello! In the previous episode, we covered fine-tuning and discussed how to adapt pre-trained models to improve accuracy for specific tasks. Fine-tuning is a powerful technique in transfer learning, enabling bette...

Recap and Today's Theme Hello! In the previous episode, we discussed data augmentation, which improves model performance even with limited data by using techniques such as rotation, shifting, and noise addition. Today, we will dive into ...

Recap and Today's Theme Hello! In the previous episode, we explained how to build image classification models using CNNs (Convolutional Neural Networks). Using the handwritten digit dataset (MNIST) as an example, we learned how to classi...

Recap and Today's Theme Hello! In the previous episode, we explained feature extraction methods like SIFT, SURF, and ORB for detecting key points in images. While these techniques are effective for object recognition and pattern matching...

Recap and Today's Theme Hello! In the previous episode, we discussed template matching, a fundamental technique for detecting specific patterns in images. Template matching is widely applied in object recognition, quality inspection, and...