现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Artificial Intelligence
前往频道在 Telegram
🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
显示更多📈 Telegram 频道 Artificial Intelligence 的分析概览
频道 Artificial Intelligence (@machinelearning_deeplearning) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 53 207 名订阅者，在 教育 类别中位列第 3 254，并在 印度 地区排名第 7 029 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 53 207 名订阅者。
根据 10 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 1 050，过去 24 小时变化为 35，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 5.80%。内容发布后 24 小时内通常能获得 1.68% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 3 086 次浏览，首日通常累积 892 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 9。
- 主题关注点： 内容集中在 learning, classification, layer, pattern, chatbot 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
凭借高频更新（最新数据采集于 11 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 教育 类别中的关键影响点。
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订阅者
+3524 小时
+1927 天
+1 05030 天
帖子存档
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2. Mock Interview Practice:
Create a mock interview scenario for the [JOB TITLE] role at [SPECIFIC COMPANY]. Include 5 common and challenging questions I might face, and provide guidance on how to answer each effectively.
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1. Developing STAR Method Responses:
Help me craft a STAR (Situation, Task, Action, Result) response to the interview question: [INSERT QUESTION] for the [JOB TITLE] role. Ensure the response is clear, concise, and demonstrates my impact in previous roles.
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Here are 10 ChatGPT-4o Prompts you need to know to Dominate and Excel at any job interview:
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8. Set up the user interface and trigger the main function.
• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"
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7. Define the main function to run all LLMs and aggregate results.
• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.
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6. Implement the LLM call function.
• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response
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5. Define the models and aggregator system prompt.
• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt
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4. Initialize Together AI clients.
• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients
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3. Set up the Streamlit app and API key input.
• Creates a title for the app
• Adds a secure input field for the Together API key
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2. Import necessary libraries
• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions
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1. Install the necessary Python Libraries
Run the following commands from your terminal to install the required libraries:
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Build an LLM app with Mixture of AI Agents using small Open Source LLMs that can beat GPT-4o in just 40 lines of Python Code (step-by-step instructions):
⬇️
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You can use ChatGPT to make money online.
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"
Read more......
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Machine_Learning_in_Finance_From_Theory_to_Practice_Matthew_F_Dixon.pdf8.75 MB
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Free ML crash course by Google
👇👇
https://developers.google.com/machine-learning/crash-course/
