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Artificial Intelligence
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🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
Show more📈 Analytical overview of Telegram channel Artificial Intelligence
Channel Artificial Intelligence (@artificial_intelligence_com) in the English language segment is an active participant. Currently, the community unites 70 390 subscribers, ranking 1 845 in the Technologies & Applications category and 4 788 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 70 390 subscribers.
According to the latest data from 12 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 1 141 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.42%. Within the first 24 hours after publication, content typically collects 2.10% reactions from the total number of subscribers.
- Post reach: On average, each post receives 5 221 views. Within the first day, a publication typically gains 1 476 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
- Thematic interests: Content is focused on key topics such as learning, linkedin, linux, udemy, 040k|.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“🔒 Welcome Artificial Intelligence Channel
Buy ads: https://telega.io/c/Artificial_Intelligence_COM”
Thanks to the high frequency of updates (latest data received on 13 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
70 390
Subscribers
+1124 hours
+2017 days
+1 14130 days
Posts Archive
70 390
🔅 Machine Learning with Python: Logistic Regression
📝 Get an introduction to logistic regression by exploring how to build supervised machine learning models with Python.
🌐 Author: Frederick Nwanganga
🔰 Level: Intermediate
⏰ Duration: 1h 19m
📋 Topics: Logistic Regression, Machine Learning, Python
🔗 Join Machine Learning for more courses
70 390
From automating repetitive tasks to boosting creativity, the best AI tools are essential for improving productivity in 2026 ✌️
70 390
RAG was supposed to make LLMs smarter.
Ground them in facts. Give them memory.
But the truth?
Most RAG systems today are just fancy search engines—fetching chunks and hoping the model figures it out.
That’s not intelligence.
The real upgrade is Agentic RAG.
Tools like Glean, Perplexity, and Harvey don’t just retrieve... they reason.
They decide what to fetch, when to fetch, or whether they should fetch anything at all.
This changes everything:
• No blind embeddings
• No random chunk dumps
• Real, layered memory
• APIs, search, and tools inside the reasoning loop
The LLM stops guessing and starts thinking.
70 390
📱Artificial Intelligence and Machine Learning
📱Introduction to Large Language Models
70 390
📱Artificial Intelligence and Machine Learning
📱Introduction to Large Language Models
70 390
🔅 Introduction to Large Language Models
📝 Learn about large language models—what they are, what they can do, and how they work.
🌐 Author: Jonathan Fernandes
🔰 Level: Intermediate
⏰ Duration: 1h 17m
📋 Topics: Large Language Models
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70 390
🔰 Python library for finetuning Gemma 3
Includes papers on finetuning, sharding, LoRA, PEFT, multimodality, and tokenization in LLM.
pip install gemma70 390
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70 390
⭐️ 5 Techniques to Fine-Tune Large Language Models (LLMs)
With the rise of large language models (LLMs), fine-tuning for specific tasks has become more important than ever. But how can we do it efficiently without compromising performance? 🤔 Here are 5 advanced techniques that can help:
1⃣ LoRA (Low-Rank Adaptation)
LoRA reduces the number of trainable parameters by adding low-rank adaptation matrices, making fine-tuning faster and more memory-efficient.🔢 LoRA-FA (LoRA with Feature Augmentation)
This method combines LoRA with external feature augmentation, injecting task-specific features to further boost performance with minimal overhead.🔢 Vera (Virtual Embedding Regularization Adaptation)
Vera helps regularize model embedding during fine-tuning, preventing over-fitting and improving generalization across different domains.🔢 Delta LoRA
An extension of LoRA, this approach focuses on updating only the most significant layers, reducing computational costs while retaining fine-tuning effectiveness.🔢 Prefix Tuning
Instead of modifying model weights, this technique learns task-specific prefix tokens that steer the model’s output, enabling efficient adaptation to new tasks.
70 390
📱Artificial Intelligence and Machine Learning
📱Machine Learning Foundations: Statistics
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🔅 Machine Learning Foundations: Statistics
📝 Learn how statistics can help you troubleshoot issues, optimize performance, and innovate, creating new machine learning models that are more efficient.
🌐 Author: Terezija Semenski
🔰 Level: Beginner
⏰ Duration: 1h 20m
📋 Topics: Statistical Analysis, Machine Learning
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70 390
SOCKS. MARKET Updates
Our Official Channel for platform updates, infrastructure changes, and service announcements related to residential and mobile proxy solutions.
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70 390
📱Artificial Intelligence and Machine Learning
📱Building a Recommendation System with Python Machine Learning and AI
70 390
🔅 Building a Recommendation System with Python Machine Learning and AI
📝 Discover how to use Python to build programs that can make recommendations. This hands-on course explores different types of recommendation systems, and shows how to build each one.
🌐 Author: Lillian Pierson, P.E.
🔰 Level: Intermediate
⏰ Duration: 1h 39m
📋 Topics: Machine Learning, Recommender Systems
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