This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.
You will:
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
This is the fourth course of the Deep Learning Specialization.
This course is part of the Deep Learning Specialization
Convolutional Neural Networks
Offered By
About this Course
4.9
20,318 ratings
Skills you will gain
Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network
Syllabus - What you will learn from this course
Week
16 hours to complete
Foundations of Convolutional Neural Networks
Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.
12 videos (Total 140 min), 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
1 practice exercise
The basics of ConvNets20m
Week
25 hours to complete
Deep convolutional models: case studies
Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
11 videos (Total 99 min), 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 practice exercise
Deep convolutional models20m
Week
34 hours to complete
Object detection
Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.
10 videos (Total 85 min), 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
1 practice exercise
Detection algorithms20m
Week
45 hours to complete
Special applications: Face recognition & Neural style transfer
Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!
11 videos (Total 76 min), 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
1 practice exercise
Special applications: Face recognition & Neural style transfer20m
38%
started a new career after completing these courses
37%
got a tangible career benefit from this course
11%
got a pay increase or promotion
Top Reviews
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.
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.
Instructors
Andrew Ng
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian Katanforoosh
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.aiAbout deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
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.
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.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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