Artificial Intelligence
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16 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 1 172 ga, so‘nggi 24 soatda esa 17 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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| 02 Iyul | +37 | |||
| 01 Iyul | +40 |
Kanal postlari
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. 😅
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖
Instead of endless Google searches, everything is organized into categories:
• fundamentals of machine learning
• neural networks and modern architectures
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• libraries and tools
• fairness and AI ethics
• production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️
🌐 https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
| 2 | ✅ Top Artificial Intelligence Concepts You Should Know 🤖🧠
🔹 1. Natural Language Processing (NLP)
Use Case: Chatbots, language translation
→ Enables machines to understand and generate human language.
🔹 2. Computer Vision
Use Case: Face recognition, self-driving cars
→ Allows machines to "see" and interpret visual data.
🔹 3. Machine Learning (ML)
Use Case: Predictive analytics, spam filtering
→ AI learns patterns from data to make decisions without explicit programming.
🔹 4. Deep Learning
Use Case: Voice assistants, image recognition
→ A type of ML using neural networks with many layers for complex tasks.
🔹 5. Reinforcement Learning
Use Case: Game AI, robotics
→ AI learns by interacting with the environment and receiving feedback.
🔹 6. Generative AI
Use Case: Text, image, and music generation
→ Models like ChatGPT or DALL·E create human-like content.
🔹 7. Expert Systems
Use Case: Medical diagnosis, legal advice
→ AI systems that mimic decision-making of human experts.
🔹 8. Speech Recognition
Use Case: Voice search, virtual assistants
→ Converts spoken language into text.
🔹 9. AI Ethics
Use Case: Bias detection, fair AI systems
→ Ensures responsible and transparent AI usage.
🔹 10. Robotic Process Automation (RPA)
Use Case: Automating repetitive office tasks
→ Uses AI to handle rule-based digital tasks efficiently.
💡 Learn these concepts to understand how AI is transforming industries!
💬 Tap ❤️ for more! | 2 030 |
| 3 |  🔰 Learn Python and Machine Learning | 3 716 |
| 4 | 📦 Exercise Files | 5 000 |
| 5 | 📱Machine Learning
📱The AI Ecosystem for Developers: Models, Datasets, and APIs | 4 901 |
| 6 |  🔅 The AI Ecosystem for Developers: Models, Datasets, and APIs
📝 This is a comprehensive guide to understanding key components of the AI ecosystem: models, datasets, and APIs.
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🔰 Level: Intermediate
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🌐 Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium | 7 511 |
| 10 | The only LLM cheat sheet you'll ever need 🚀
Covers the main concepts, architectures, and practical applications.
Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
Transformer architecture and its variants
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Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
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- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling) | 7 198 |
| 11 | 📱Machine Learning
📱AI Sentiment Analysis with PyTorch and Hugging Face Transformers | 8 710 |
| 12 |  🔅 AI Sentiment Analysis with PyTorch and Hugging Face Transformers
📝 Build and deploy a sentiment analysis model using Hugging Face Transformers and PyTorch.
🌐 Author: Zhongyu Pan
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⏰ Duration: 32m
📋 Topics: PyTorch, Sentiment Analysis
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| 13 |  👍 Top 6 Types of AI Models | 8 423 |
| 14 |  🚀 8 Types of AI Agents You Should Know
AI agents are evolving beyond just text generation. Different architectures are being designed to specialize in reasoning, perception, action, and abstraction. Here’s a quick breakdown:
1️⃣ GPTs – general-purpose text generators, great for fluency and versatility.
2️⃣ MoE (Mixture of Experts) – route tasks to specialized subnetworks for efficiency.
3️⃣ Large Reasoning Models – optimized for multi-step logical reasoning.
4️⃣ Vision-Language Models – bridge perception and language for multimodal tasks.
5️⃣ Small Language Models – lightweight, cost-efficient agents for edge deployment.
6️⃣ Large Action Models – built to execute code, call APIs, and perform tasks autonomously.
7️⃣ Hierarchical Language Models – break problems into sub-tasks, enabling long-horizon planning.
8️⃣ Large Concept Models – capture abstract, high-level knowledge for generalization.
🔍 What this really shows is that “AI agents” are no longer a monolithic idea. They’re evolving into a system of complementary architectures—each optimized for a different layer of intelligence. | 8 724 |
| 15 |  📱 Understanding Machine learning algorithms | 7 924 |
| 16 | 📦 Exercise Files | 8 427 |
| 17 | 📱Machine Learning
📱Natural Language Processing with PyTorch | 8 663 |
| 18 |  🔅 Natural Language Processing with PyTorch
📝 Learn the basics of using PyTorch, a powerful deep learning tool, for natural language processing.
🌐 Author: Zhongyu Pan
🔰 Level: Intermediate
⏰ Duration: 41m
📋 Topics: Natural Language Processing, PyTorch
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