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Description
Let's translate the course into Russian, by doing so we will:
- Help people overcome the language barrier 🤝.
- Contribute to a more even spread of technology in the world🌎 and accelerate technological progress 🚀.
Below are the chapters and files that need translation - let us know here if you want to translate any of them and we will add your name to the list. Once you're done, open a translation request and tag that issue by specifying #issue-number in the description, where issue-number is the number of that issue.
If you would like to contribute, join us. Together we 🚀 )))
Our translation team
Coordinator (someone to ask questions, who is leading the issue): @artyomboyko, ...
Reviewer (someone who can help make things better): @burtenshaw
Translator (someone who does translation): @artyomboyko, ...
Translation agreement (contains organizational aspects that the team follows)
Units
Unit 0. Welcome to the course
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introduction.mdx- Welcome to the course - @artyomboyko -
onboarding.mdx- Onboarding - @artyomboyko -
discord101.mdx- (Optional) Discord 101 - @artyomboyko
Live 1. How the course works and Q&A
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live1.mdx- Live 1: How the Course Works and First Q&A - @artyomboyko
Unit 1. Introduction to Agents
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introduction.mdx- Introduction - @artyomboyko -
what-are-agents.mdx- What is an Agent? - @artyomboyko -
quiz1.mdx- Quick Quiz 1 - @artyomboyko -
what-are-llms.mdx- What are LLMs? - @artyomboyko -
messages-and-special-tokens.mdx- Messages and Special Tokens - @artyomboyko -
tools.mdx- What are Tools? - @artyomboyko -
quiz2.mdx- Quick Quiz 2 - @artyomboyko -
agent-steps-and-structure.mdx- Understanding AI Agents through the Thought-Action-Observation Cycle - @artyomboyko -
thoughts.mdx- Thought: Internal Reasoning and the Re-Act Approach - @artyomboyko -
actions.mdx- Actions: Enabling the Agent to Engage with Its Environment - @artyomboyko -
observations.mdx- Observe: Integrating Feedback to Reflect and Adapt - @artyomboyko -
dummy-agent-library.mdx- Dummy Agent Library - @artyomboyko -
tutorial.mdx- Let’s Create Our First Agent Using smolagents - @artyomboyko -
final-quiz.mdx- Unit 1 Quiz - @artyomboyko -
get-your-certificate.mdx- Get your certificate - @artyomboyko -
conclusion.mdx- Conclusion - @artyomboyko
Unit 2. Frameworks for AI Agents
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introduction.mdx- Introduction to Agentic Frameworks - @artyomboyko
Unit 2.1 The smolagents framework
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introduction.mdx- Introduction to smolagents - @artyomboyko -
why_use_smolagents.mdx- Why use smolagents - @artyomboyko -
quiz1.mdx- Quick Quiz 1 - @artyomboyko -
code_agents.mdx- Building Agents That Use Code - @artyomboyko -
tool_calling_agents.mdx- Writing actions as code snippets or JSON blobs - @artyomboyko -
tools.mdx- Tools - @artyomboyko -
retrieval_agents.mdx- Building Agentic RAG Systems - @artyomboyko -
quiz2.mdx- Quick Quiz 2 - @artyomboyko -
multi_agent_systems.mdx- Multi-Agent Systems - @artyomboyko -
vision_agents.mdx- Vision Agents with smolagents - @artyomboyko -
final_quiz.mdx- Final Quiz - @artyomboyko -
conclusion.mdx- Conclusion - @artyomboyko
Bonus Unit 1. Fine-tuning an LLM for Function-calling
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introduction.mdx- Introduction - @artyomboyko -
what-is-function-calling.mdx- What is Function Calling? - @artyomboyko -
fine-tuning.mdx- Let’s Fine-Tune Your Model for Function-Calling - @artyomboyko -
conclusion.mdx- Conclusion - @artyomboyko
When the next steps are published?
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next-units.mdx- Next units - @artyomboyko