About this Course

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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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 20 hours to complete

English

Subtitles: Chinese (Traditional), Chinese (Simplified), Korean, Turkish, English, Spanish, Japanese...

Skills you will gain

Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network

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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 20 hours to complete

English

Subtitles: Chinese (Traditional), Chinese (Simplified), Korean, Turkish, English, Spanish, Japanese...

Syllabus - What you will learn from this course

Content RatingThumbs Up94%(39,929 ratings)Info
Week
1

Week 1

6 hours to complete

Foundations of Convolutional Neural Networks

6 hours to complete
12 videos (Total 140 min), 4 readings, 3 quizzes
12 videos
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
4 readings
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
2

Week 2

5 hours to complete

Deep convolutional models: case studies

5 hours to complete
11 videos (Total 99 min), 1 reading, 2 quizzes
11 videos
Classic Networks18m
ResNets7m
Why ResNets Work9m
Networks in Networks and 1x1 Convolutions6m
Inception Network Motivation10m
Inception Network8m
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
3

Week 3

4 hours to complete

Object detection

4 hours to complete
10 videos (Total 85 min), 2 readings, 2 quizzes
10 videos
Landmark Detection5m
Object Detection5m
Convolutional Implementation of Sliding Windows11m
Bounding Box Predictions14m
Intersection Over Union4m
Non-max Suppression8m
Anchor Boxes9m
YOLO Algorithm7m
(Optional) Region Proposals6m
2 readings
Convolutional Implementation of Sliding Windows *CORRECTION*1m
YOLO algorithm *CORRECTION*1m
1 practice exercise
Detection algorithms30m
Week
4

Week 4

5 hours to complete

Special applications: Face recognition & Neural style transfer

5 hours to complete
11 videos (Total 76 min), 3 readings, 3 quizzes
11 videos
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
3 readings
Triplet Loss *CORRECTION*1m
Face Verification and Binary Classification *CORRECTION*1m
Style Cost *CORRECTION*1m
1 practice exercise
Special applications: Face recognition & Neural style transfer30m

About the Deep Learning Specialization

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI....
Deep Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • 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.