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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-ai-with-python-and-reinforcement-learning/
课程评论：没有评论
**Coursera 课程总结：Python 与强化学习实践人工智能** 这门由 Jose 主讲的 Coursera 课程，旨在通过 Python 教授实用的 AI 知识，特别是神经网络和强化学习。课程处于“早鸟”发布阶段，内容仍在不断完善中。 **课程亮点：** * **实践导向：** 与许多仅展示小型玩具式示例的课程不同，本课程强调动手实践，让学习者能够将 AI 应用于自己的问题和环境中。 * **核心技术：** 涵盖了人工智能的关键领域，包括： * 人工神经网络 (ANN) * 卷积神经网络 (CNN) * 经典 Q-Learning * 深度 Q-Learning * Sarsa * 交叉熵方法 * Double DQN * 以及更多。 * **深度强化学习：** 课程的最终目标是使学习者能够独立创建自己的深度强化学习智能体（agents），能在自定义的环境中运行。 * **理论与实践结合：** 在理论讲解与代码实现之间寻求平衡，通过清晰的幻灯片示例将数学公式与实际代码相结合，并指导学习者手动实现强化学习算法。 * **技术栈：** 首先介绍 Keras 和 TensorFlow 中的深度学习，然后深入讲解强化学习概念（如 Q-Learning），最后融合两者，演示深度 Q-Network (DQN) 等深度强化学习智能体的构建。 **目标用户：** 渴望理解人工智能工作原理，并希望利用神经网络和强化学习创建能够解决复杂任务的智能体的 Python 学习者。 **特别提示：** 课程尚未完全完成，目前处于早期发布阶段，新内容将持续更新。
Please note! This course is in an "early bird" release, and we're still updating and adding content to it, please keep in mind before enrolling that the course is not yet complete."The future is already here - it's just not very evenly distributed."Have you ever wondered how Artificial Intelligence actually works? Do you want to be able to harness the power of neural networks and reinforcement learning to create intelligent agents that can solve tasks with human level complexity?This is the ultimate course online for learning how to use Python to harness the power of Neural Networks to create Artificially Intelligent agents!This course focuses on a practical approach that puts you in the driver's seat to actually build and create intelligent agents, instead of just showing you small toy examples like many other online courses. Here we focus on giving you the power to apply artificial intelligence to your own problems, environments, and situations, not just those included in a niche library!This course covers the following topics:Artificial Neural NetworksConvolution Neural NetworksClassical Q-LearningDeep Q-LearningSARSACross Entropy MethodsDouble DQNand much more!We've designed this course to get you to be able to create your own deep reinforcement learning agents on your own environments. It focuses on a practical approach with the right balance of theory and intuition with useable code. The course uses clear examples in slides to connect mathematical equations to practical code implementation, before showing how to manually implement the equations that conduct reinforcement learning.We'll first show you how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as Q-Learning. Then we can combine these ideas to walk you through Deep Reinforcement Learning agents, such as Deep Q-Networks!There is still a lot more to come, I hope you'll join us inside the course!Jose
