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Updated track to speakers PyDay25
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content/events/pyday_bcn/pyday_bcn_2025.md

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Al finalizar, serás capaz de:
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- Entender la arquitectura básica de un agente LLM (razonamiento + acción).
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- Implementar un bucle de control para que el LLM pueda usar herramientas.
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- Permitir al modelo ejecutar funciones de Python."
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start_time_slot: 11:30
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end_time_slot: 12:45
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track_length: 1
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topic: "Data Science"
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location: "Aula Hady Lamarr"
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requirements: "Participants should bring a laptop with:
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- Python 3.10+ installed
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- Ollama installed with Llama 3.2 model downloaded
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- Basic familiarity with Python
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- 8GB+ RAM recommended
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No prior experience with AI agents or Pydantic AI required."
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# repositories: "https://github.com/ericmassip/carbon-home-watcher"
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Al finalizar, serás capaz de:
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- Implementar un índice invertido simple y un buscador basado en keywords.
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- Entender y programar un sistema de ranking con TF‑IDF / BM25.
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- Utilizar vectores y similitud de coseno para búsqueda semántica básica.
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- Comparar enfoques y saber cuándo usar palabras clave, BM25 o vectores."
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start_time_slot: 14:00
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end_time_slot: 15:15

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