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π€ Python libraries for AI agents β 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 β 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 β simple agents, tools, and memory
github.com/langchain-ai/langchain
CrewAI β multi-agent systems with roles
github.com/joaomdmoura/crewAI
SmolAgents β lightweight agents for quick experiments
github.com/huggingface/smolagents
LangGraph β orchestration and stateful workflow
github.com/langchain-ai/langgraph
LlamaIndex β RAG and knowledge-agents
github.com/run-llama/llama_index
Semantic Kernel β AI workflow and plugins
github.com/microsoft/semantic-kernel
AutoGen β autonomous multi-agent systems
github.com/microsoft/autogen
DSPy β optimizing LLM pipelines
github.com/stanfordnlp/dspy
A2A β protocol for interaction between agents
github.com/a2aproject/A2A
https://t.me/CodeProgrammerπ
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 β 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 β simple agents, tools, and memory
github.com/langchain-ai/langchain
CrewAI β multi-agent systems with roles
github.com/joaomdmoura/crewAI
SmolAgents β lightweight agents for quick experiments
github.com/huggingface/smolagents
LangGraph β orchestration and stateful workflow
github.com/langchain-ai/langgraph
LlamaIndex β RAG and knowledge-agents
github.com/run-llama/llama_index
Semantic Kernel β AI workflow and plugins
github.com/microsoft/semantic-kernel
AutoGen β autonomous multi-agent systems
github.com/microsoft/autogen
DSPy β optimizing LLM pipelines
github.com/stanfordnlp/dspy
A2A β protocol for interaction between agents
github.com/a2aproject/A2A
https://t.me/CodeProgrammer
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Forwarded from Machine Learning with Python
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 β from LLM and RAG to AI agents and MCP.
All resources are here: aka.ms/pythonai/resources
π @codeprogrammer
Now you can calmly rewatch the recordings, download the slides, and try the code from each session β from LLM and RAG to AI agents and MCP.
All resources are here: aka.ms/pythonai/resources
Please open Telegram to view this post
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π₯Whether you're preparing for #Python, #AI, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification β SPOTO has got you covered!
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Do you want to understand the methods used to train LLMs?
The training of large language models (LLMs) is based on various approaches that help models understand and generate text.
Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.
Here are 4 common methods of training LLMs in simple language π
1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.
2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.
3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".
4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.
These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful.
https://t.me/CodeProgrammer
The training of large language models (LLMs) is based on various approaches that help models understand and generate text.
Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.
Here are 4 common methods of training LLMs in simple language π
1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.
2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.
3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".
4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.
These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful.
https://t.me/CodeProgrammer
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π· Category: it-and-software
π Language: English (US)
π₯ Students: 17,221 students
βοΈ Rating: 4.5/5.0 (113 reviews)
πββοΈ Enrollments Left: 48
β³ Expires In: 0D:10H:10M
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π Coupon: F888C355AA9260F585D7
β οΈ Please note: A verification layer has been added to prevent bad actors and bots from claiming the courses, so it is important for genuine users to enroll manually to not lose this free opportunity.
π By: https://t.me/DataScienceC