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所在平台: Udemy |
课程主页: https://www.udemy.com/course/neural-networks-from-scratch-in-java/
课程评论：没有评论
课程名称：人工智能 II - 动手神经网络（Java） 课程概述：本课程聚焦于人工神经网络，随着人工智能和机器学习的逐渐普及，神经网络的重要性日益凸显。尽管早期支持向量机等技术一度超越神经网络，但进入21世纪后，神经网络再次受到关注。尽管训练过程较慢，神经网络在多个领域具有强大的应用潜力，包括回归问题、光学字符识别和人脸检测等。 课程内容： 第一部分：神经网络概述 - 神经网络定义 - 模拟人脑 - 宏观视角 第二部分：反向传播算法 - 前馈神经网络 - 成本函数优化 - 错误计算 - 反向传播与梯度下降 第三部分：单个感知器模型 - 解决线性分类问题 - 逻辑运算符（AND与XOR运算） 第四部分：神经网络的应用 - 聚类 - 分类（鸢尾花数据集） - 光学字符识别（OCR） - 从零开始构建微笑检测应用 在课程的第一部分，你将学习神经网络的理论基础，随后将掌握其具体实现方法。如果你渴望学习这些方法，就让我们开始吧！
This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21th century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection. Section 1:what are neural networksmodeling the human brainthe big pictureSection 2:what is back-propagationfeedforward neural networksoptimizing the cost functionerror calculationbackpropagation and gradient descentSection 3:the single perceptron modelsolving linear classification problemslogical operators (AND and XOR operation)Section 4:applications of neural networksclusteringclassification (Iris-dataset)optical character recognition (OCR)smile-detector application from scratchIn the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them. If you are keen on learning methods, let's get started!In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them. If you are keen on learning methods, let's get started!
