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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-design-of-a-neural-network-in-cstep-by-step/
课程评论：没有评论
**课程名称：** 神经网络 C++ 实战项目教程 **课程概述：** 本课程旨在教授如何使用 C++ 实际设计和构建一个神经网络模拟器。课程内容涵盖了从个体神经元（Neuron）的类设计，到使用向量（Vectors）实现神经元层，再到最终构建包括输入层、隐藏层和输出层的完整神经网络结构。 课程将通过随机生成的训练样本，展示数据如何通过输入层（以向量形式输入）进行前向传播（Feed forward），最终传递到输出层。此外，课程还将实现反向传播（Back propagation）算法，用于计算误差并更新权重，以提升神经网络的准确性。课程会详细讲解训练样本的生成原理，使学员在课程结束后能够自行生成真实样本进行测试。 **核心概念：** * 前向传播 (Feed forward) * 偏置神经元 (Bias Neuron) * 迁移函数 (Transfer function) * 反向传播 (Back propagation) * 激活函数 (Activation function) * 均方根误差 (Root mean square error) * 迁移函数导数 (Transfer function derivative) * 生成训练样本 (Generating training samples) * 输出层和隐藏层梯度 (Output and hidden layer Gradient) **C++ 核心概念：** * `Assert()` * 原型设计 (prototyping) * 类设计 (Class design) * 嵌套向量 (Nested Vectors) * 引用变量 (Reference Variables) * 静态类变量 (Static class variables) * 数据隐藏与封装 (Data hiding and encapsulation) **适用人群：** 建议所有对神经网络有理论知识、并希望将其付诸实践的 C++ 程序员参加。
This course teaches the practical design of a Neural network simulator using C++. It is recommended for all levels of C++ programmers with a theoretical knowledge of Neural network and looking forward to implement them in practice. The course interactively simulates the Neural network from the design of the class called Neuron, to the implementation of the Neuron layers in Vectors and finally the top level design consisting of the input layer, hidden layer and the output layer. Some random training samples will be generated which will be feed to the input layer through a vector and progress to the output layer through feed forward. The back propagation is also implemented which enables us to calculate the error and update the weight for a more accurate result. The training samples used in this course is for demonstration as the concept of sample generation is well explained. At the end of the course the student should be able generate real samples for testings. Some of the Core concepts we will learn in this course includes:Feed forward.Bias Neuron.Transfer function.Back propagation.Activation function.Root mean square error.Transfer function derivative.Generating training samples.Output and hidden layer Gradient.Some of the C++ concepts used includes:Assert()prototypingClass designNested VectorsReference VariablesStatic class variablesData hiding and encapsulation
