In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
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深度学习 专项课程deeplearning.ai课程信息
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第 4 门课程（共 5 门）
中级
- Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
- A basic grasp of linear algebra & ML
完成时间大约为41 小时
英语（English）
字幕：中文（繁体）, 阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 中文（简体）, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）, 日语
您将获得的技能
- Deep Learning
- Facial Recognition System
- Convolutional Neural Network
- Tensorflow
- Object Detection and Segmentation
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 4 门课程（共 5 门）
中级
- Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
- A basic grasp of linear algebra & ML
完成时间大约为41 小时
英语（English）
字幕：中文（繁体）, 阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 中文（简体）, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）, 日语
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deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授课大纲 - 您将从这门课程中学到什么
完成时间为 11 小时
Foundations of Convolutional Neural Networks
Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.
完成时间为 11 小时
12 个视频 （总计 140 分钟）, 6 个阅读材料, 5 个测验
完成时间为 10 小时
Deep Convolutional Models: Case Studies
Discover some powerful practical tricks and methods used in deep CNNs, straight from the research papers, then apply transfer learning to your own deep CNN.
完成时间为 10 小时
14 个视频 （总计 127 分钟）, 2 个阅读材料, 4 个测验
完成时间为 10 小时
Object Detection
Apply your new knowledge of CNNs to one of the hottest (and most challenging!) fields in computer vision: object detection.
完成时间为 10 小时
14 个视频 （总计 110 分钟）, 3 个阅读材料, 4 个测验
完成时间为 9 小时
Special Applications: Face recognition & Neural Style Transfer
Explore how CNNs can be applied to multiple fields, including art generation and face recognition, then implement your own algorithm to generate art and recognize faces!
完成时间为 9 小时
11 个视频 （总计 75 分钟）, 4 个阅读材料, 4 个测验
审阅
- 5 stars87.69%
- 4 stars10.41%
- 3 stars1.43%
- 2 stars0.28%
- 1 star0.17%
来自CONVOLUTIONAL NEURAL NETWORKS的热门评论
由 AM 提供May 5, 2019
A big thank you to Professor Andrew and his team for structuring this course and introducing the world of ConvNets to me. I found the video lectures easy to understand and the exercises intriguing.
由 AY 提供Apr 2, 2021
Elated to have spent several weeks on such a wonderfully constructed course. Wonder if ther future versions will include SOTAs like mobile networks and efficientnets... Thank you very much, Dr. Ng!
由 AL 提供Jun 2, 2018
The videos were very good. I came into the course knowing a bit from doing my own readings and the material covered in the course really helped me understand how convolutional neural networks work.
由 AR 提供Jul 11, 2020
I really enjoyed this course, it would be awesome to see al least one training example using GPU (maybe in Google Colab since not everyone owns one) so we could train the deepest networks from scratch
关于 深度学习 专项课程
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
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