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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Includes hands on learning projects.

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Course 4 of 5 in the

Deep Learning Specialization

Intermediate Level

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

Approx. 36 hours to complete

English

Skills you will gain

Deep Learning
Facial Recognition System
Convolutional Neural Network
Tensorflow
Object Detection and Segmentation

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 5 in the

Deep Learning Specialization

Intermediate Level

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

Approx. 36 hours to complete

English

Instructors

Instructor rating

4.92/5 (2,960 Ratings)

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Content Rating95%(66,267 ratings)

Week1

Week 1

10 hours to complete

Foundations of Convolutional Neural Networks

10 hours to complete

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

12 videos

Computer Vision5mEdge Detection Example11mMore Edge Detection7mPadding9mStrided Convolutions8mConvolutions Over Volume10mOne Layer of a Convolutional Network16mSimple Convolutional Network Example8mPooling Layers10mCNN Example12mWhy Convolutions?9mYann LeCun Interview27m

5 readings

Clarifications about Upcoming Simple Convolutional Network Example Video1mClarifications about Upcoming CNN Example Video1mClarifications about Upcoming Why Convolutions?1mHow to Download your Notebook2mHow to Refresh your Workspace5m

1 practice exercise

The Basics of ConvNets 50m

Week2

Week 2

9 hours to complete

Deep Convolutional Models: Case Studies

9 hours to complete

14 videos (Total 127 min), 2 readings, 4 quizzes

14 videos

Why look at case studies?2mClassic Networks18mResNets7mWhy ResNets Work?9mNetworks in Networks and 1x1 Convolutions6mInception Network Motivation10mInception Network8mMobileNet16mMobileNet Architecture8mEfficientNet3mUsing Open-Source Implementation4mTransfer Learning8mData Augmentation9mState of Computer Vision12m

2 readings

Clarifications about Upcoming Inception Network Motivation Video1mNote on the Upcoming Programming Assignment - Residual Networks1m

1 practice exercise

Deep Convolutional Models 50m

Week3

Week 3

9 hours to complete

Object Detection

9 hours to complete

14 videos (Total 110 min), 3 readings, 4 quizzes

14 videos

Object Localization11mLandmark Detection5mObject Detection5mConvolutional Implementation of Sliding Windows11mBounding Box Predictions14mIntersection Over Union4mNon-max Suppression8mAnchor Boxes9mYOLO Algorithm6mRegion Proposals (Optional)6mSemantic Segmentation with U-Net7mTranspose Convolutions7mU-Net Architecture Intuition3mU-Net Architecture7m

3 readings

Clarifications about Upcoming Convolutional Implementation of Sliding Windows Video1mClarifications about Upcoming YOLO Algorithm Video1mClear Output Before Submitting (For U-Net Assignment)10m

1 practice exercise

Detection Algorithms 50m

Week4

Week 4

9 hours to complete

Special Applications: Face recognition & Neural Style Transfer

9 hours to complete

11 videos (Total 75 min), 4 readings, 4 quizzes

11 videos

What is Face Recognition?4mOne Shot Learning4mSiamese Network4mTriplet Loss15mFace Verification and Binary Classification6mWhat is Neural Style Transfer?2mWhat are deep ConvNets learning?7mCost Function3mContent Cost Function3mStyle Cost Function13m1D and 3D Generalizations9m

4 readings

Clarifications about Upcoming Face Verification and Binary Classification Video1mClarifications about Upcoming Style Cost Function Video1mReferences10mAcknowledgments10m

1 practice exercise

Special Applications: Face Recognition & Neural Style Transfer50m

Reviews

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5509 reviews

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4 stars
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3 stars
1.43%
2 stars
0.28%
1 star
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TOP REVIEWS FROM CONVOLUTIONAL NEURAL NETWORKS

by DGFeb 13, 2018

Too much hand-holding during assignments, although still very good directions. Obviously the issue with the final programming assignment needs to be addressed. Fantastic lecture material, as always.

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.

by DDFeb 6, 2020

very easy to understand and helped my understanding in Deep Learning-based computer vision. Yet, this course will need to be updated with new developments in the future (to catch up with the trend).

by AYApr 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!

View all reviews

About the Deep Learning Specialization

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.

In this Specialization, you will build and train 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. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also 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. 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.

More questions? Visit the Learner Help Center.

Convolutional Neural Networks

About this Course

Skills you will gain

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

Foundations of Convolutional Neural Networks

Deep Convolutional Models: Case Studies

Object Detection

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

Skills you will gain

Instructors

Andrew NgTop Instructor

Kian KatanforooshTop Instructor

Younes Bensouda MourriTop Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Foundations of Convolutional Neural Networks

Deep Convolutional Models: Case Studies

Object Detection

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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Learn Something New

Popular Data Science Courses

Popular Computer Science & IT Courses

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Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Younes Bensouda Mourri
Top Instructor