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Machine Learning with Neural Networks

Machine Learning with Neural Networks

本课程是 Practical Machine Learning: Foundations to Neural Networks 专项课程的一部分

位教师：Peter Chin

包含在中

了解更多

7个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

攻读学位

了解更多

7个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

攻读学位

了解更多

您将学到什么

How to build and train neural networks with backpropagation and regularization, and model predictive uncertainty using Bayesian neural networks.

您将获得的技能

您将学习的工具

Tensorflow

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

25 项作业

授课语言：英语（English）

91% of learners achieved a positive career outcome

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Practical Machine Learning: Foundations to Neural Networks 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有7个模块

This course explores the principles of machine learning through the lens of one of its most powerful and versatile model classes: the artificial neural network. We will cover the fundamental machine learning concepts of modeling, training, and generalization. You will learn how to process the input data with feed-forward operations, how to train a neural network model using gradient-based optimization and the backpropagation algorithm, and how to ensure it performs well on new data using regularization. In the final module, we discuss Bayesian neural networks, learning how to build models that not only make predictions but also quantify their own uncertainty.