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所在平台: Udemy |
课程主页: https://www.udemy.com/course/neuroevolution-of-augmenting-topologies-neat/
课程评论：没有评论
课程名称：增强拓扑的神经进化 NEAT 神经网络 课程概述： 本课程是对增强拓扑的神经进化（NEAT）算法的入门介绍，重点讲解了构成NEAT算法的最重要概念。课程将理论与实际应用相结合，特别是在机器人技术和控制领域的真实问题中应用基于遗传算法的人工神经网络。课程适合各类背景和知识水平的学习者，结构设计合理，无需高级先修知识。 课程重点内容包括： - 神经网络简介 - 遗传算法简介 - 编码 - 繁殖/交叉 - 突变 - 种类化 - 维度处理 - 实现 - 应用 此外，课程还包含了如何使用NEAT算法的Python实现来构建自己的NEAT神经网络的教程。学习者只需掌握基本的Python知识，即可开始设置训练环境和演化过程，从而系统地生成高效的神经网络。所需的仅是一款简单的代码编辑器和你专注的态度。 课程由一位拥有高级学位的学术研究者授课，将帮助你深入理解NEAT，从其工作原理到如何使用该算法进化自己的神经网络。
This is an introductory course to the NeuroEvolution of Augmenting Topologies algorithm. The course covers the most important concepts that characterize the NEAT algorithm, where a focus on understanding the theory behind genetic-algorithm-based artificial neural networks and their application to real-world problems, particularly in the fields of robotics and control. This course is intended for individuals from all backgrounds and knowledge levels, as it is structured such that there are no advanced prerequisites. From the fundamental concepts of neural networks to the unique mechanisms found in the algorithm, the lectures provide a succinct and complete overview of NEAT that can be understood by any researcher, academic, or self-learner.The list of topics covered include:Introduction to neural networksIntroduction to genetic algorithmsEncodingReproduction/crossoverMutationSpeciationDimensionalityImplementationApplicationThis series also includes a tutorial on how to implement your own NEAT-based neural networks using a Python implementation of the algorithm. Only basic Python knowledge is required to get started on setting up the training environment and evolutionary process to procedurally generate efficient neural networks. All that is required is a simple code editor and your attention.Taught by an academic researcher with advanced degrees, this course will familiarize you to NEAT, from how it works to how to use it to evolve your own neural networks.
