Okay, I need to create a professional Wikipedia-style document about CNN under the category of Artificial Intelligence and Machine Learning. Let me start by understanding the requirements.

First, the structure: H1 for the title, an overview section, several sections with H2 and H3 headings, and maybe references or related documents. The content needs to be accurate, objective, detailed enough, easy to read, and use technical terms with explanations. Also, markdown formatting with proper hierarchy, lists, tables, code blocks if needed, and at least 800 characters.

The title is "CNN" under the subcategory of Machine Learning. So I should start by defining what CNN is. Maybe start with an introduction explaining its purpose in AI/ML. Then break down into sections like Architecture, Applications, Training Process, Advantages/Disadvantages, and maybe a section on related technologies.

Wait, the user mentioned "기술" (technology) as the main category, "인공지능" (AI), and "머신러닝" (Machine Learning). So CNN is a type of neural network used in ML. I should explain its structure, how it works, applications, training methods, pros and cons.

For the overview section, I need to briefly introduce CNNs, their significance in image processing, maybe mention they are a subset of deep learning. Then under H2 sections: Architecture (layers like Convolutional, Pooling, Fully Connected), Applications (image classification, object detection, etc.), Training Process (backpropagation, optimization algorithms), Advantages and Disadvantages.

I should also include examples or code snippets if possible. Maybe a simple example of a CNN architecture using PyTorch or TensorFlow. But since the user didn't specify code, maybe just mention it in a section. Also, need to make sure technical terms are explained. For instance, "convolutional layer" – explain what that is.

Check for accuracy: CNNs use filters (kernels) to extract features, pooling reduces spatial dimensions, etc. Make sure the information is up-to-date and correct. Avoid any subjective statements; stick to facts.

Need to ensure the document is over 800 characters. Let me count. The example response provided earlier was around that length. I need to elaborate each section sufficiently.

Possible sections:

1. Introduction/Overview
2. Architecture of CNNs (H2)
3. Convolutional Layer (H3)
4. Pooling Layer (H3)
5. Fully Connected Layer (H3)
6. Applications (H2)
7. Training Process (H2)
8. Advantages and Disadvantages (H2)
9. Related Technologies (H2)

Include a references section if needed, but maybe not necessary unless the user specifies.

Make sure each H2 has enough content. For example, under Architecture, explain each layer's role, how they work together. In applications, list different use cases with examples.

Avoid markdown in the thinking process, but when writing the actual document, use proper headings, lists, etc.

Check for clarity and flow. Use simple language where possible, but don't oversimplify technical terms. For example, explain "kernel" as a filter that slides over the input to detect features.

Also, mention popular CNN architectures like LeNet, AlexNet, VGG, ResNet if relevant. Maybe in the applications or architecture section.

Need to ensure that each part is detailed enough. For instance, under training process, discuss backpropagation, loss functions (like cross-entropy), optimization algorithms (SGD, Adam), and maybe data augmentation techniques.

Make sure the document is comprehensive but not redundant. Avoid repetition. Each section should add value.

Check for any possible errors: e.g., CNNs are primarily used in image processing, but also in other domains like NLP with 1D convolutions. Mention that if applicable.

Alright, time to structure the markdown document accordingly.