|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/convolutional-neural-network/
课程评论：没有评论
**课程名称：卷积神经网络 (Convolutional Neural Network)** **课程概述：** 本课程深入探讨人工智能领域，专注于一种强大的深度学习技术——卷积神经网络（CNN）。通过模仿人类视觉皮层的神经活动，CNN能够让计算机学习并成为特定领域的专家。课程首先介绍深度学习的基本概念和CNN的分类，阐述CNN的定义及其优势。随后，课程将指导学员如何设计和构建自己的CNN架构，介绍所需的硬件和软件工具，并展示一些著名的CNN模型。最后，课程将讨论CNN的局限性以及未来的发展趋势和挑战。 **核心要点：** * **深度学习入门：** 什么是深度学习？为何使用计算智能算法？ * **CNN的灵感来源：** CNN如何从大脑的生物模拟中获得启发？ * **CNN的分类与算法：** 深度学习的分类，CNN的种类及定义。 * **CNN的优势与目的：** 卷积神经网络的核心优势以及其在人工智能中的作用。 * **CNN的架构与训练：** CNN的典型架构、参数训练与优化方法。 * **CNN的工具生态：** 用于CNN设计的硬件和软件资源。 * **实践应用与前沿：** 著名的CNN架构、CNN的应用领域、CNN的局限性以及未来的挑战与机遇。 * **课程总结：** 本课程的重点回顾。
Artificial intelligence is a large field that includes many techniques to make machines think, which means endowing this machine with intelligence, unlike, as we all know, the habitual intelligence exhibited by humans and animals. Therefore, in this course, we investigate the mimicking of human intelligence on machines by introducing a modern algorithm of artificial intelligence named the convolutional neural network, which is a technique of deep learning for computers to make the machine learn and become an expert. In this course, we present an overview of deep learning in which, we introduce the notion and classification of convolutional neural networks. We also give the definition and the advantages of CNNs. In this course, we provide the tricks to elaborate your own architecture of CNN and the hardware and software to design a CNN model. In the end, we present the limitations and future challenges of CNN.The essential points tackled in this course are illustrated as follows:- What is deep learning?- Why are computational intelligence algorithms used?- Biomimetics inspiration of CNN from the brain- Classification of deep learning (CNN)- The kinds of deep learning algorithms- Definition of convolutional neural networks- Advantages of Convolutional Neural Network- The pupose of CNN- Architecture of CNN- Training and optimization of CNN parameters- Hardware material used for CNN- Software used for deep learning- Famous CNN architecture- Application of CNN- Limitation of CNN- Future and challenges of convolutional neural networks- Conclusions
