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programación práctica

  • Berkeley Pacman Projects

    An open-source Python framework featuring Pacman-based AI agents for implementing search, adversarial, and reinforcement learning algorithms.
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    What is Berkeley Pacman Projects?
    The Berkeley Pacman Projects repository offers a modular Python codebase where users build and test AI agents in a Pacman maze. It guides learners through uninformed and informed search (DFS, BFS, A*), adversarial multi-agent search (minimax, alpha-beta pruning), and reinforcement learning (Q-learning with feature extraction). Integrated graphical interfaces visualize agent behavior in real time, while built-in test cases and an autograder verify correctness. By iterating on algorithm implementations, users gain practical experience in state space exploration, heuristic design, adversarial reasoning, and reward-based learning within a unified game framework.

  • QuickStart

    Learn JavaScript quickly and efficiently with QuickStartJS for developers at any level.
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    What is QuickStart?
    QuickStartJS is an educational platform aimed at facilitating quick, efficient, and effective learning of JavaScript. It offers a structured curriculum that includes practical examples, interactive exercises, and numerous resources tailored to developers of various experience levels. Whether you are a beginner just starting out, or an experienced developer aiming to refine your skills, QuickStartJS has educational modules that will guide you through essential concepts and advanced topics in JavaScript.

  • Agents Course by Justinvarghese511

    A hands-on course teaching developers to build AI agents using LangChain for task automation, document retrieval, and conversational workflows.
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    What is Agents Course by Justinvarghese511?
    Agents Course by Justinvarghese511 is a structured learning program that equips developers with the skills to architect, implement, and deploy AI agents. Through step-by-step tutorials, participants learn to design agent decision flows, integrate external APIs, and manage context and memory. The course includes hands-on code examples, Jupyter notebooks, and practical exercises for building agents that automate data extraction, respond conversationally, and perform multi-step tasks. By the end, learners will have a portfolio of working AI agent projects and best practices for production deployment.

  • LangChain with Python Bootcamp

    Hands-on bootcamp teaching developers to build AI Agents with LangChain and Python through practical labs.
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    What is LangChain with Python Bootcamp?
    This bootcamp covers the LangChain framework end-to-end, enabling you to build AI Agents in Python. You’ll explore prompt templates, chain composition, agent tooling, conversational memory, and document retrieval. Through interactive notebooks and detailed exercises, you’ll implement chatbots, automated workflows, question-answering systems, and custom agent chains. By course end, you’ll understand how to deploy and optimize LangChain-based agents for diverse tasks.
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