Okay, I need to create a Wikipedia-style document about LSTM in the context of machine learning under the science category. 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 headers, lists, tables, code blocks if needed, and at least 800 characters.

Starting with the title: "LSTM" as H1. Then an introduction that explains what LSTM is, its purpose, and maybe a brief history. Next sections could be on architecture, applications, advantages/disadvantages, training process, and related techniques. Maybe include examples or code snippets if possible.

Wait, the user mentioned to use markdown with H2 and H3. So after the intro, I can have sections like "1. 개요", "2. 구조 및 작동 원리" (which would be H2), then under that, H3 for components like memory cell, gates, etc. Then maybe a section on applications, another on challenges, and references.

Need to make sure technical terms are explained. For example, when talking about gates (input, forget, output), explain what they do in simple terms. Also, mention the difference between RNNs and LSTMs since that's a common point of confusion.

Including examples might help. Maybe an example of how LSTM processes sequences, or a code snippet using Python with TensorFlow/Keras to show how it's implemented. But I should check if that's necessary. The user didn't specify code, but the instructions say to use code blocks if there are technical contents. So maybe include a simple example.

Also, need to ensure the document is over 800 characters. Let me count as I go. Each section should be detailed enough. For instance, in the architecture section, explain each component thoroughly. In applications, list different fields where LSTM is used, like NLP, time series prediction, etc.

Check for accuracy: LSTMs were introduced by Hochreiter & Schmidhuber in 1997. Mention that. Also, their purpose was to solve the vanishing gradient problem in RNNs. Explain how they do that through gates and memory cells.

Potential sections:

• Introduction (Overview)
• History and Development
• Architecture and Components (H2 with H3 for each part: Memory Cell, Gates, etc.)
• Training Process
• Applications
• Advantages and Disadvantages
• Challenges and Limitations
• Related Techniques (like GRU, Transformers)
• References

Make sure to use proper markdown syntax. Avoid markdown in the thinking process but structure it correctly in the final answer.

Need to avoid any markdown in the thinking part, just the final output. Also, ensure that each section is well-explained with examples where possible. Maybe include a table comparing RNN and LSTM if relevant.

Check for technical terms: "gates" (input, forget, output), "memory cell", "hidden state", "cell state". Explain these in simple terms.

Also, mention the use of activation functions like sigmoid and tanh in gates. Maybe explain how each gate works.

In the applications section, give specific examples like language modeling, machine translation, speech recognition, etc.

For challenges, maybe talk about computational complexity, overfitting, or the need for large datasets.

References could include the original paper by Hochreiter & Schmidhuber, and some modern resources or frameworks that use LSTM.

Now, putting it all together in markdown format with proper headings, sections, and content. Ensure each part flows logically and meets the requirements.