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所在平台: Udemy |
课程主页: https://www.udemy.com/course/basics-of-neural-networks-your-ultimate-beginners-guide/
课程评论：没有评论
**课程名称：** 神经网络基础：你的终极入门指南 **课程概述：** 本课程是为完全零基础的学习者精心设计的神经网络入门指南。讲师 Rim Zakhama 是一位人工智能专家，拥有数学与计算机科学博士学位，热衷于将复杂的AI概念变得易于理解。 **课程特点：** * **极度适合初学者：** 无需任何机器学习或深度学习背景。 * **内容精炼易懂：** 以清晰简洁的语言和生动形象的例子，将神经网络的核心概念拆解成易于吸收的部分。 * **侧重理解：** 课程侧重概念的讲解和理解，虽包含大量示例，但不设练习，让学习者专注于掌握知识。 **学习目标：** 学完本课程后，你将： * 理解神经网络是什么，以及它们如何模仿人脑。 * 掌握神经网络的关键组成部分，如激活函数、权重、偏置和损失函数。 * 了解前向传播过程，以及神经网络如何进行预测。 * 熟悉神经网络训练的基本步骤。 **课程内容（基础概念）：** * 神经网络的定义与类比人脑 * 激活函数 * 权重与偏置 * 损失函数 * 前向传播 * 神经网络训练过程（将避免复杂的数学细节，如梯度下降算法） 本课程旨在为你打下坚实的神经网络基础，让你能够初步理解构建能够从数据中学习和做出决策的系统。
Welcome to the most beginner-friendly introduction to Neural Networks!My name is Rim Zakhama, your instructor for this course. I am an AI expert with a PhD in Applied Mathematics and Computer Science. I also hold a Master's degree in Computer Science and an Engineering degree. My passion is to make complex AI concepts accessible and easy to understand for everyone.If you're looking to understand the basics of Neural Networks in a simplified and time-efficient way, you're in the right place. This course is tailored for absolute beginners, requiring no prior knowledge of machine learning or deep learning.With clear and concise explanations, this course breaks down key Neural Network concepts into digestible pieces. You'll learn through simple explanations and relatable examples, making it easy to grasp the core ideas. While the course includes many examples to illustrate the concepts, it does not include exercises, allowing you to focus entirely on understanding the material.By the end of this journey, you'll have a solid understanding of the fundamental concepts of neural networks and how they work. This knowledge will empower you to build systems that can learn and make decisions from data.We will cover foundational concepts such as:What neural networks are and how they mimic the human brain.Key components like activation functions, weights, biases, and loss functions.The process of forward propagation and how neural networks make predictions.Steps involved in training a neural network.While we will cover the steps involved in training a neural network, we will avoid delving into complex mathematics, such as gradient descent algorithm, to ensure the material remains accessible to all learners.
