What real-world applications of CNN are covered in this course?

This course demonstrates CNN applications in image classification, object detection, and visual recognition tasks, preparing learners for projects in fields like healthcare imaging, autonomous vehicles, and retail analytics.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Introduction to CNN Training

Introduction to CNN Training

位教师：Priyanka Mehta

包含在 Coursera Plus 中

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2个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Understand CNN fundamentals and their role in deep learning and computer vision.
Explore key components like convolution, ReLU, and pooling layers.
Learn how CNNs recognize and process images for classification tasks.
Gain hands-on experience building and implementing CNN models through guided labs.

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该课程共有2个模块

This beginner-friendly course on Convolutional Neural Networks (CNNs) equips you with essential skills to understand deep learning fundamentals and apply them to real-world image recognition tasks. Learn how CNNs power modern AI applications and gain practical experience through guided lab demos. Build confidence in designing, training, and implementing CNN models effectively.

By the end of this course, you will be able to: Understand CNN Basics: Explain what CNNs are and their role in deep learning and computer vision Explore Core Components: Learn about convolution, ReLU, and pooling layers in CNNs Recognize Image Processing: Understand how CNNs detect and classify image features Apply CNN Models: Build and implement CNN models through hands-on guided labs Gain Practical Skills: Develop expertise to handle real-world image classification projects Ideal for beginners, and professionals interested in AI, computer vision, and deep learning.

Understand the fundamentals of Convolutional Neural Networks (CNNs) to build expertise in deep learning and computer vision. Learn how CNNs recognize images and explore core components like convolution, ReLU, and pooling layers. Gain practical skills through guided lab demos and implement CNN models for real-world image classification tasks.

涵盖的内容

8个视频1篇阅读材料3个作业1个插件

8个视频总计7分钟

What is CNN?1分钟
How Does CNN Recognizes Images?0分钟
Layers in CNN0分钟
Convolution Layer0分钟
ReLU Layer0分钟
Pooling Layer1分钟
Structure of CNN0分钟
How Exactly CNN Recognize an Image?0分钟

1篇阅读材料总计10分钟

Course Syllabus10分钟

3个作业总计59分钟

Quiz on Introduction to CNN6分钟
Quiz on Core Components and Structure of CNNs15分钟
Assessment for Fundamentals of CNN38分钟

1个插件总计15分钟

Understanding CNN Fundamentals: Layers and Image Recognition15分钟

Gain practical experience in Convolutional Neural Networks through step-by-step lab demos. From basics to advanced implementation, this module walks you through multiple hands-on exercises across five demos, helping you build, test, and apply CNN models effectively in real-world scenarios.

涵盖的内容

5个视频3个作业1个插件

5个视频总计58分钟

Hands on Lab Demo Part 113分钟
Hands on Lab Demo Part 213分钟
Hands on Lab Demo Part 313分钟
Hands on Lab Demo Part 411分钟
Hands on Lab Demo Part 57分钟

3个作业总计17分钟

Quiz on Practical Implementation - I12分钟
Quiz on Practical Implementation - II3分钟
Assessment for Hands-On with CNN2分钟

1个插件总计15分钟

Hands-On Implementation of CNNs: Reflection and Application15分钟

位教师

Priyanka Mehta

Simplilearn

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常见问题

CNN training teaches the fundamentals of Convolutional Neural Networks, including their structure, components, and practical applications in image recognition.

You’ll learn to build, train, and implement CNN models using layers like convolution, ReLU, and pooling for real-world image classification tasks.

Yes, you will receive a certificate upon successfully completing the course and its hands-on projects.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.