Okay, I need to create a professional Wikipedia-style document about Res in Korean, following the user's. Let me start by understanding requirements. The main category is Technology, then Artificial Intelligence, followed by Machine Learning, and the title is ResNet. The should have a clear titleH1), an introduction, several detailed sections (H2, H3), and possibly references. It needs to be accurate, objective, with sufficient depth, easy to read, and use proper markdown formatting.

First, the title should be "ResNet" as H1. The introduction should give a brief overview of what ResNet is. I remember that ResNet stands for Residual Network, introduced in 2015 by Kaiming He and his team. It's known for the residual learning framework, which uses skip connections to address the vanishing gradient problem in deeper networks. That's important to mention here.

Next, the detailed sections. The user mentioned using H2 and H3. Let me think about the main topics. The key concepts would be the residual learning framework, residual blocks, and how they work. Then, maybe a section on the architecture details, like the different versions of ResNet (e.g., ResNet-18, ResNet-50) and their structures. Applications and impact is another important section, discussing where ResNet has been used and its influence on the field. Advantages and limitations would be good too, to provide a balanced view.

I need to ensure technical terms are explained. For example, when talking about skip connections, I should briefly explain what they are. Also, mention the problem of vanishing gradients in deep networks and how residual blocks help mitigate that by allowing gradients to flow through shortcuts.

In the architecture section, maybe include a table comparing different ResNet versions, their number of layers, and block configurations. That would be useful. Also, explain the structure of a basic block versus a bottleneck block, especially for deeper networks like ResNet-50.

Applications: ResNet is widely used in computer vision tasks like image classification, object detection, semantic segmentation. Mention competitions like ILSVRC 2015 where it performed well. Also, maybe touch on its use in real-world applications beyond academia.

Advantages: Solves vanishing gradient, enables deeper networks, parameter efficiency, robustness. Limitations: Computational cost for very deep versions, maybe not the best for all tasks, and the fact that newer architectures might outperform it now.

For references, include the original paper and any notable follow-up works or resources. Also, link to other related concepts like CNNs, DenseNet for further reading.

I should check that the document is over 800 characters. Let me make sure each section has enough content. Avoid overly technical jargon where possible, but since it's a professional document, some terms are necessary with explanations.

Wait, the user specified markdown. So, use # for H1, ## for H2, ### for H3. Code blocks for any technical code examples, but since ResNet is more about architecture, maybe not necessary unless discussing implementation details. But the user might not expect actual code here, just structure.

Also, make sure the document flows logically. Start with introduction, then concepts, architecture, applications, advantages/limitations, and references. Each section should have enough detail without being too verbose.

Need to verify the accuracy of the information. For example, the residual blocks use ReLU and batch normalization. The skip connection can be an identity function or a 1x1 convolution if dimensions differ. That's correct. The original paper used a pre-activation variant, but the standard ResNet uses two or three convolutional layers in the block.

In the applications section, mention specific datasets like ImageNet, COCO. Also, note that ResNet variants are used as backbones in other models.

Limitations: Even though ResNet allows deeper networks, very deep ones might still have issues with training time or resources. Also, while skip connections help, they might not be the most efficient design compared to later architectures like DenseNet.

References should include the original papers: "Deep Residual Learning for Image Recognition" by He et al., and maybe the follow-up on pre-activation. Also, link to frameworks like TensorFlow or PyTorch implementations if relevant, but maybe just the papers are sufficient.

I think that's a solid outline. Now, structure the document accordingly in Korean, using proper markdown. Check for clarity, correct terminology, and that each section meets the. Ensure that the total length is adequate. Let me start drafting each part step by step, making sure to explain each concept clearly and maintain a professional yet accessible tone.