Machine Learning with Python

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The Python + Generative AI series by Azure AI Foundry has ended, but all materials are open Now you can calmly rewatch the recordings, download the slides, and try the code from each session — from LLM and RAG to AI agents and MCP. All resources are here: aka.ms/pythonai/resources 👉  @codeprogrammer

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🤖 Python libraries for AI agents — what to study If you want to develop AI agents in Python, it's important to understand the order of studying libraries. Start with LangChain, CrewAI or SmolAgents — they allow you to quickly assemble simple agents, connect tools, and test ideas. The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows. The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines. LangChain — simple agents, tools, and memory  github.com/langchain-ai/langchain CrewAI — multi-agent systems with roles  github.com/joaomdmoura/crewAI SmolAgents — lightweight agents for quick experiments  github.com/huggingface/smolagents LangGraph — orchestration and stateful workflow  github.com/langchain-ai/langgraph LlamaIndex — RAG and knowledge-agents  github.com/run-llama/llama_index Semantic Kernel — AI workflow and plugins  github.com/microsoft/semantic-kernel AutoGen — autonomous multi-agent systems  github.com/microsoft/autogen DSPy — optimizing LLM pipelines  github.com/stanfordnlp/dspy A2A — protocol for interaction between agents  github.com/a2aproject/A2A https://t.me/CodeProgrammer 🌟

PhD Students - Do you need datasets for your research? Here are 30 datasets for research from NexData. Use discount code for 20% off: G5W924C3ZI 1. Korean Exam Question Dataset for AI Training https://lnkd.in/d_paSwt7 2. Multilingual Grammar Correction Dataset https://lnkd.in/dV43iqTp 3. High quality video caption dataset https://lnkd.in/dY9kxkhx 4. 3D models and scenes datasets for AI and simulation https://lnkd.in/dT-zscH4 5. Image editing datasets – object removal, addition & modification https://lnkd.in/dd8iCGMS 6. QA dataset – visual & text reasoning https://lnkd.in/dc3TNWFD 7. English instruction tuning dataset https://lnkd.in/dTeTgd2M 8. Large scale vision language dataset for AI training https://lnkd.in/dBJuxazN 9. News dataset https://lnkd.in/dYBJe5gd 10. Global building photos dataset https://lnkd.in/dVJsDXnC 11. Facial landmarks dataset https://lnkd.in/dz_KGCS4 12. 3D Human Pose & Landmarks dataset https://lnkd.in/dXE9ir8Z 13. 3D Hand Pose & Gesture Recognition dataset https://lnkd.in/d_QdGGb9 14. 14. Driver monitoring dataset – dangerous, fatigue https://lnkd.in/d6kF-9PW 15. Japanese handwriting OCR dataset https://lnkd.in/dHnriqrH 16. American English Male voice TTS dataset https://lnkd.in/dqyvg862 17. Riddles and brain teasers dataset https://lnkd.in/dKBHY3DE 18. Chinese test questions text https://lnkd.in/dQpUd8xC 19. Chinese medical question answering data https://lnkd.in/dsbWUCpz 20. Multi-round interpersonal dialogues text data https://lnkd.in/dQiUq_Jg 21. Human activity recognition dataset https://lnkd.in/dHM52MfV 22. Facial expression recognition dataset https://lnkd.in/dqQAfMau 23. Urban surveillance dataset https://lnkd.in/dc2RCnTk 24. Human body segmentation dataset https://lnkd.in/d6sSrDxS 25. Fashion segmentation – clothing & accessories https://lnkd.in/dptNUTz8 26. Fight video dataset – action recognition https://lnkd.in/dnY_m5hZ 27. Gesture recognition dataset https://lnkd.in/dFVPivYg 28. Facial skin defects dataset https://lnkd.in/dKCbUvU6 29. Smoke detection and behaviour recognition dataset https://lnkd.in/ddGg56R4 30. Weight loss transformation video dataset https://lnkd.in/dqqT4ed9 https://t.me/CodeProgrammer 👾

PhD Students - Do you need datasets for your research? Here are 30 datasets for research from NexData. Use discount code for 20% off: G5W924C3ZI 1. Korean Exam Question Dataset for AI Training https://lnkd.in/d_paSwt7 2. Multilingual Grammar Correction Dataset https://lnkd.in/dV43iqTp 3. High quality video caption dataset https://lnkd.in/dY9kxkhx 4. 3D models and scenes datasets for AI and simulation https://lnkd.in/dT-zscH4 5. Image editing datasets – object removal, addition & modification https://lnkd.in/dd8iCGMS 6. QA dataset – visual & text reasoning https://lnkd.in/dc3TNWFD 7. English instruction tuning dataset https://lnkd.in/dTeTgd2M 8. Large scale vision language dataset for AI training https://lnkd.in/dBJuxazN 9. News dataset https://lnkd.in/dYBJe5gd 10. Global building photos dataset https://lnkd.in/dVJsDXnC 11. Facial landmarks dataset https://lnkd.in/dz_KGCS4 12. 3D Human Pose & Landmarks dataset https://lnkd.in/dXE9ir8Z 13. 3D Hand Pose & Gesture Recognition dataset https://lnkd.in/d_QdGGb9 14. 14. Driver monitoring dataset – dangerous, fatigue https://lnkd.in/d6kF-9PW 15. Japanese handwriting OCR dataset https://lnkd.in/dHnriqrH 16. American English Male voice TTS dataset https://lnkd.in/dqyvg862 17. Riddles and brain teasers dataset https://lnkd.in/dKBHY3DE 18. Chinese test questions text https://lnkd.in/dQpUd8xC 19. Chinese medical question answering data https://lnkd.in/dsbWUCpz 20. Multi-round interpersonal dialogues text data https://lnkd.in/dQiUq_Jg 21. Human activity recognition dataset https://lnkd.in/dHM52MfV 22. Facial expression recognition dataset https://lnkd.in/dqQAfMau 23. Urban surveillance dataset https://lnkd.in/dc2RCnTk 24. Human body segmentation dataset https://lnkd.in/d6sSrDxS 25. Fashion segmentation – clothing & accessories https://lnkd.in/dptNUTz8 26. Fight video dataset – action recognition https://lnkd.in/dnY_m5hZ 27. Gesture recognition dataset https://lnkd.in/dFVPivYg 28. Facial skin defects dataset https://lnkd.in/dKCbUvU6 29. Smoke detection and behaviour recognition dataset https://lnkd.in/ddGg56R4 30. Weight loss transformation video dataset https://lnkd.in/dqqT4ed9 https://t.me/CodeProgrammer 👾

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CNN vs Vision Transformer — The Battle for Computer Vision 👁⚡️ Two architectures. One goal: identify the cat. But they see things differently: 🧠 CNN (Convolutional Neural Network) · Scans the image with filters · Detects local patterns first (edges → textures → shapes) · Builds understanding layer by layer 🔄 Vision Transformer (ViT) · Splits image into patches (like words in a sentence) · Detects global patterns from the start · Sees the whole picture using attention mechanisms Same input. Same output. Different journey. CNNs think locally and build up. Transformers think globally from the get-go. Which one wins? Depends on the task — but both are shaping the future of how machines see. https://t.me/CodeProgrammer

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How a CNN sees images simplified 🧠 1. Input → Image breaks into pixels (RGB numbers) 2. Feature Extraction · Convolution → Detects edges/patterns · ReLU → Kills negatives, adds non-linearity · Pooling → Shrinks data, keeps what matters 3. Fully Connected → Flattens features into meaning 4. Output → Probability scores: Cat? Dog? Car? Why powerful: Learns hierarchically — edges → shapes → objects Pixels to predictions. That's it. 👇 #DeepLearning #CNN #ComputerVision #AI

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Python Tip: Operator Overloading This is a very important concept in Python.

Have you ever wondered how #Python understands what the + operator means? For numbers, it's addition; for strings, it's concatenation; for lists, it's union. This is operator overloading in action. Operator overloading means defining special behavior for operators (+, -, *, ==, etc.) in your user-defined classes. You determine how these operators should work with your objects.

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