Convolutional Neural Networks

This course is part of the Deep Learning Specialization

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Andrew Ng +2 more instructors
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Deep Learning SpecializationDeepLearning.AI

About this Course

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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.

Learner Career Outcomes

36%

started a new career after completing these courses

37%

got a tangible career benefit from this course

12%

got a pay increase or promotion

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 4 of 5 in the

Deep Learning Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML

Approx. 34 hours to complete

English

Subtitles: Chinese (Traditional), Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish, Japanese

Skills you will gain

Deep LearningFacial Recognition SystemConvolutional Neural NetworkTensorflowObject Detection and Segmentation

Learner Career Outcomes

36%

started a new career after completing these courses

37%

got a tangible career benefit from this course

12%

got a pay increase or promotion

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 5 in the

Deep Learning Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML

Approx. 34 hours to complete

English

Subtitles: Chinese (Traditional), Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish, Japanese

Instructors

Instructor rating

4.92/5 (2,249 Ratings)

Offered by

DeepLearning.AI

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Syllabus - What you will learn from this course

Content Rating95%(56,819 ratings)

Week

Week 1

9 hours to complete

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.

9 hours to complete

12 videos (Total 140 min), 5 readings, 3 quizzes

12 videos

Computer Vision5m

Edge Detection Example11m

More Edge Detection7m

Padding9m

Strided Convolutions9m

Convolutions Over Volume10m

One Layer of a Convolutional Network16m

Simple Convolutional Network Example8m

Pooling Layers10m

CNN Example12m

Why Convolutions?9m

Yann LeCun Interview27m

5 readings

Connect with your Mentors and Fellow Learners on Discourse!10m

Strided convolutions *CORRECTION*1m

Simple Convolutional Network Example *CORRECTION*1m

CNN Example *CORRECTION*1m

Why Convolutions? *CORRECTION*1m

1 practice exercise

The Basics of ConvNets30m

Week

Week 2

9 hours to complete

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.

9 hours to complete

14 videos (Total 128 min), 1 reading, 3 quizzes

14 videos

Why look at case studies?3m

Classic Networks18m

ResNets7m

Why ResNets Work?9m

Networks in Networks and 1x1 Convolutions6m

Inception Network Motivation10m

Inception Network8m

MobileNet16m

MobileNet Architecture8m

EfficientNet3m

Using Open-Source Implementation4m

Transfer Learning8m

Data Augmentation9m

State of Computer Vision12m

1 reading

Inception Network Motivation *CORRECTION*1m

1 practice exercise

Deep Convolutional Models30m

Week

Week 3

8 hours to complete

Object Detection

Apply your new knowledge of CNNs to one of the hottest (and most challenging!) fields in computer vision: object detection.

8 hours to complete

14 videos (Total 111 min), 2 readings, 3 quizzes

14 videos

Object Localization11m

Landmark Detection5m

Object Detection5m

Convolutional Implementation of Sliding Windows11m

Bounding Box Predictions14m

Intersection Over Union4m

Non-max Suppression8m

Anchor Boxes9m

YOLO Algorithm7m

Region Proposals (Optional)6m

Semantic Segmentation with U-Net7m

Transpose Convolutions7m

U-Net Architecture Intuition3m

U-Net Architecture7m

2 readings

Convolutional Implementation of Sliding Windows *CORRECTION*1m

YOLO algorithm *CORRECTION*1m

1 practice exercise

Detection Algorithms30m

Week

Week 4

8 hours to complete

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!

8 hours to complete

11 videos (Total 76 min), 5 readings, 3 quizzes

11 videos

What is Face Recognition?4m

One Shot Learning4m

Siamese Network4m

Triplet Loss15m

Face Verification and Binary Classification6m

What is Neural Style Transfer?2m

What are deep ConvNets learning?7m

Cost Function3m

Content Cost Function3m

Style Cost Function13m

1D and 3D Generalizations9m

5 readings

Triplet Loss *CORRECTION*1m

Face Verification and Binary Classification *CORRECTION*1m

Style Cost *CORRECTION*1m

References10m

Acknowledgments10m

1 practice exercise

Special Applications: Face Recognition & Neural Style Transfer30m

Reviews

4.9

5070 reviews

5 stars
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4 stars
10.54%
3 stars
1.45%
2 stars
0.28%
1 star
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TOP REVIEWS FROM CONVOLUTIONAL NEURAL NETWORKS

by SHAug 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.

by RSDec 11, 2019

Great Course Overall One thing is that some videos are not edited properly so Andrew repeats the same thing, again and again, other than that great and simple explanation of such complicated tasks.

by RKSep 1, 2019

This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.

by AGJan 12, 2019

Great course for kickoff into the world of CNN's. Gives a nice overview of existing architectures and certain applications of CNN's as well as giving some solid background in how they work internally.

View all reviews

About the Deep Learning Specialization

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.

In this Specialization, you will build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing. AI is transforming many industries. The Deep Learning Specialization 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. Along the way, you will get career advice from deep learning experts from industry and academia.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

More questions? Visit the Learner Help Center.

Convolutional Neural Networks

About this Course

Learner Career Outcomes

36%

37%

12%

Skills you will gain

Learner Career Outcomes

36%

37%

12%

Instructors

Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Younes Bensouda Mourri
Top Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Week 1

Foundations of Convolutional Neural Networks

Week 2

Deep Convolutional Models: Case Studies

Week 3

Object Detection

Week 4

Special Applications: Face recognition & Neural Style Transfer

Reviews

TOP REVIEWS FROM CONVOLUTIONAL NEURAL NETWORKS

About the Deep Learning Specialization

Frequently Asked Questions

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Convolutional Neural Networks

About this Course

Learner Career Outcomes

36%

37%

12%

Skills you will gain

Learner Career Outcomes

36%

37%

12%

Instructors

Andrew NgTop Instructor

Kian KatanforooshTop Instructor

Younes Bensouda MourriTop Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Week 1

Foundations of Convolutional Neural Networks

Week 2

Deep Convolutional Models: Case Studies

Week 3

Object Detection

Week 4

Special Applications: Face recognition & Neural Style Transfer

Reviews

TOP REVIEWS FROM CONVOLUTIONAL NEURAL NETWORKS

About the Deep Learning Specialization

Frequently Asked Questions

Coursera Footer

Top Online Courses

Top Online Specializations

Online Certificates

Online Degree Programs

Coursera

Community

More

Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Younes Bensouda Mourri
Top Instructor