Okay, I need to create a professional Wikipedia-style document about "Deep Learning" under the categories of Technology > Artificial Intelligence > 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, detailed, easy to understand, and use technical terms with explanations. Also, it should be over 800 characters long.

Starting with the title: "딥러닝" as H1. Then an introduction that explains what deep learning is, its relation to machine learning, and maybe a brief history. Next sections could cover architecture, applications, challenges, and future directions. Each section can have sub-sections (H3) for more detail.

I should make sure to define terms like neural networks, layers, activation functions, etc., in simple terms. Maybe include examples of deep learning models like CNNs or RNNs. Also, mention real-world applications such as image recognition, NLP, autonomous vehicles. Challenges could be data requirements, computational power, overfitting. Future directions might involve explainability, ethical concerns, and advancements in algorithms.

Need to check for technical accuracy. For example, explaining how deep learning differs from traditional machine learning by using multiple layers. Mentioning specific architectures like CNNs, RNNs, GANs. Also, include some key concepts like backpropagation, gradient descent.

I should avoid being too vague. Each section needs enough detail but not overly complex. Use bullet points or lists where appropriate, maybe a table comparing different types of neural networks. Ensure that the language is clear and concise, avoiding jargon without explanation.

Check for length: 800 characters is about 150-200 words. The example provided in the user's response is longer, so I need to make sure each section is sufficiently detailed. Maybe include subsections under each main H2 heading. For instance, under "딥러닝의 핵심 구성 요소" (Core Components), have H3 sections on neural networks, layers, activation functions.

Also, think about the flow: introduction, core concepts, architecture, applications, challenges, future directions. Each part should logically follow the previous one. Maybe add a section on training processes and optimization techniques.

Need to verify that all information is accurate. For example, confirming that deep learning uses multiple hidden layers, and explaining how each layer contributes to feature extraction. Mentioning specific frameworks like TensorFlow or PyTorch if relevant, but maybe not necessary unless it's part of the content.

Avoid any markdown formatting except for the specified structure. Use H1, H2, H3 headings, lists, tables, code blocks if needed. Since the user mentioned code blocks for technical content, perhaps include a simple example of a neural network in pseudocode or a snippet using a framework like Keras.

Check that all sections are covered and meet the requirements. Make sure each part is well-explained but not too verbose. Balance between depth and readability. Use examples to illustrate points, like how CNNs are used in image recognition tasks.

Finally, review for grammar and clarity, ensuring that technical terms are explained so that readers with varying levels of expertise can understand. Maybe add a references section if citing specific sources, but since it's a general overview, perhaps not necessary unless the user specifies.