|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/build-ai-agents/
课程评论：没有评论
课程名称：用印地语（Hinglish）构建人工智能代理 课程概述：你是否对构建可以独立思考、计划和行动的真实人工智能代理感到兴奋，就像一个虚拟团队成员一样？在这个基于项目的实践课程中，你将学习如何使用Python、OpenAI（GPT-4）、Transformers和Autogen从零开始构建智能自运行的人工智能代理。课程内容不仅限于此，你将深入探讨现代代理背后的核心机器学习技术，首先从自然语言处理（NLP）、强化学习（RL）和近端策略优化（PPO）入手，这些都是用于实际代理训练的强大策略梯度方法。 无论你是开发者、数据科学家还是人工智能爱好者，这门课程都将为你提供一份完整的端到端蓝图，帮助你构建可以从反馈中学习并自主与周围世界互动的代理，支持工具、API甚至其他代理之间的交互。你将学习如何： - 使用Python从零开始构建自己的人工智能代理框架 - 利用大型语言模型（如GPT-4、Claude和开源模型）进行智能规划 - 应用Transformers和NLP技术处理和理解语言 - 使用强化学习（RL）和PPO训练代理，实现目标导向行为 - 使用Autogen协调多代理系统，实现自主和协作 通过这门课程的学习，你不仅将了解人工智能代理的工作原理，还将构建和训练自己代理，结合深度学习、NLP、Transformers和强化学习，创造可以自适应、学习并独立运作的系统。这门课程非常适合那些想要超越被动语言模型，进入主动智能人工智能系统领域的学习者。
Are you excited about building real AI agents that can think, plan, and act autonomously-just like a virtual teammate?In this hands-on, project-based course, you'll learn to build intelligent, self-operating AI agents from scratch using Python, OpenAI (GPT-4), Transformers, and Autogen. But we go much deeper than that.You'll explore the core machine learning techniques that power modern agents-starting with Natural Language Processing (NLP), Reinforcement Learning (RL) and Proximal Policy Optimization (PPO), one of the most powerful policy-gradient methods used in real-world agent training.Whether you're a developer, data scientist, or AI enthusiast, this course gives you a complete, end-to-end blueprint to build agents that can learn from feedback, and autonomously interact with the world around them-via tools, APIs, and even other agents.You'll learn how to:Build your own AI agent framework from scratch in PythonUse LLMs (like GPT-4, Claude, and open-source models) for intelligent planningApply Transformers and NLP techniques to process and understand languageTrain agents using Reinforcement Learning (RL) and PPO for goal-directed behaviorUse Autogen to coordinate multi-agent systems with autonomy and collaborationBy the end of this course, you won't just know how AI agents work-you'll have built and trained your own, combining deep learning, NLP, transformers, and reinforcement learning to create systems that adapt, learn, and operate independently.This course is ideal for anyone looking to move beyond passive language models and into the world of active, intelligent AI systems.
