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

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Shareable Certificate

Earn a Certificate upon completion

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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.

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 (2,929 Ratings)

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Content Rating95%(65,893 ratings)

Week

Week 1

10 hours to complete

Foundations of Convolutional Neural Networks

10 hours to complete

12 videos (Total 140 min), 6 readings, 5 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

6 readings

Clarifications about Upcoming Strided Convolutions Video1m

Clarifications about Upcoming Simple Convolutional Network Example Video1m

Clarifications about Upcoming CNN Example Video1m

Clarifications about Upcoming Why Convolutions?1m

How to Download your Notebook2m

How to Refresh your Workspace5m

1 practice exercise

The Basics of ConvNets 50m

Week

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?2m

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

2 readings

Clarifications about Upcoming Inception Network Motivation Video1m

Note on the Upcoming Programming Assignment - Residual Networks1m

1 practice exercise

Deep Convolutional Models 50m

Week

Week 3

9 hours to complete

Object Detection

9 hours to complete

14 videos (Total 110 min), 3 readings, 4 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 Algorithm6m

Region Proposals (Optional)6m

Semantic Segmentation with U-Net7m

Transpose Convolutions7m

U-Net Architecture Intuition3m

U-Net Architecture7m

3 readings

Clarifications about Upcoming Convolutional Implementation of Sliding Windows Video1m

Clarifications about Upcoming YOLO Algorithm Video1m

Clear Output Before Submitting (For U-Net Assignment)10m

1 practice exercise

Detection Algorithms 50m

Week

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?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

4 readings

Clarifications about Upcoming Face Verification and Binary Classification Video1m

Clarifications about Upcoming Style Cost Function Video1m

References10m

Acknowledgments10m

1 practice exercise

Special Applications: Face Recognition & Neural Style Transfer50m

Reviews

4.9

5494 reviews

5 stars
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4 stars
10.37%
3 stars
1.42%
2 stars
0.28%
1 star
0.17%

TOP REVIEWS FROM CONVOLUTIONAL NEURAL NETWORKS

by AMMay 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.

by EBNov 2, 2017

Wonderful course. Covers a wide array of immediately appealing subjects: from object detection to face recognition to neural style transfer, intuitively motivate relevant models like YOLO and ResNet.

by ALJun 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.

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.

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

Coursera Footer

Start or advance your career

Popular Courses and Certifications

Popular collections and articles

Earn a degree or certificate online

Coursera

Community

More

Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Younes Bensouda Mourri
Top Instructor