When you enroll in this course, you'll also be enrolled in this Specialization.
Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate
There are 4 modules in this course
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.
By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
The Deep Learning Specialization is our 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. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!•2 minutes
Clarifications about Upcoming Simple Convolutional Network Example Video•1 minute
Clarifications about Upcoming CNN Example Video•1 minute
Clarifications about Upcoming Why Convolutions?•1 minute
Lecture Notes W1•1 minute
(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace•5 minutes
1 assignment•Total 50 minutes
The Basics of ConvNets •50 minutes
2 programming assignments•Total 360 minutes
Convolutional Model, Step by Step•180 minutes
Convolution Model Application•180 minutes
Deep Convolutional Models: Case Studies
Week 2•9 hours to complete
Module details
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.
Convolutional Implementation of Sliding Windows•11 minutes
Bounding Box Predictions•14 minutes
Intersection Over Union•4 minutes
Non-max Suppression•8 minutes
Anchor Boxes•10 minutes
YOLO Algorithm•7 minutes
Region Proposals (Optional)•6 minutes
Semantic Segmentation with U-Net•7 minutes
Transpose Convolutions•8 minutes
U-Net Architecture Intuition•3 minutes
U-Net Architecture•8 minutes
4 readings•Total 13 minutes
Clarifications about Upcoming Convolutional Implementation of Sliding Windows Video•1 minute
Clarifications about Upcoming YOLO Algorithm Video•1 minute
Lecture Notes W3•1 minute
Clear Output Before Submitting (For U-Net Assignment)•10 minutes
1 assignment•Total 50 minutes
Detection Algorithms •50 minutes
2 programming assignments•Total 360 minutes
Car detection with YOLO•180 minutes
Image Segmentation with U-Net•180 minutes
Special Applications: Face recognition & Neural Style Transfer
Week 4•9 hours to complete
Module details
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!
DeepLearning.AI is an education technology company that develops a global community of AI talent.
DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
4.9
42,585 reviews
5 stars
87.83%
4 stars
10.27%
3 stars
1.40%
2 stars
0.28%
1 star
0.19%
Showing 3 of 42585
S
SH
4·
Reviewed on Aug 5, 2019
Great content in lectures! Automatic graders for programming assignments can be tricky, and set to old versions of tf sometimes, but answers to these issues are readily found in the discussion forums.
F
FH
5·
Reviewed on Jan 11, 2019
Amazing! Feels like AI is getting tamed in my hands. Course lectures , assignments are excellent. To those who are not well versed with python - numpy and tensorflow , it would be better to brush up.
N
NK
5·
Reviewed on Jul 10, 2024
Fabulously designed, I could confidently say that the programming exercise is sufficiently sophisticated, and yet managed to be not so difficult as to deter new learners. All in all a great course!
When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. 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.