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.
제공자:
About this Course
최근 조회 1,047,060회
학습자 경력 결과
36%
이 강좌를 수료한 후 새로운 경력 시작하기
37%
이 강좌를 통해 확실한 경력상 이점 얻기
12%
급여 인상 또는 승진하기
귀하가 습득할 기술
Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network
학습자 경력 결과
36%
이 강좌를 수료한 후 새로운 경력 시작하기
37%
이 강좌를 통해 확실한 경력상 이점 얻기
12%
급여 인상 또는 승진하기
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 21시간 필요
권장: 4 weeks of study, 4-5 hours/week
영어
자막: 중국어 (번체자), 중국어 (간체자), 한국어, 터키어, 영어, 스페인어, 일본어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 6시간 필요
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.
완료하는 데 6시간 필요
12개 동영상 (총 140분), 4 readings, 3 quizzes
12개의 동영상
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
4개의 읽기 자료
Strided convolutions *CORRECTION*1m
Simple Convolutional Network Example *CORRECTION*1m
CNN Example *CORRECTION*1m
Why Convolutions? *CORRECTION*1m
1개 연습문제
The basics of ConvNets30m
완료하는 데 5시간 필요
Deep convolutional models: case studies
Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
완료하는 데 5시간 필요
11개 동영상 (총 99분), 1 reading, 2 quizzes
11개의 동영상
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
1개의 읽기 자료
Inception Network Motivation *CORRECTION*1m
1개 연습문제
Deep convolutional models30m
완료하는 데 4시간 필요
Object detection
Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.
완료하는 데 4시간 필요
10개 동영상 (총 85분), 2 readings, 2 quizzes
10개의 동영상
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
2개의 읽기 자료
Convolutional Implementation of Sliding Windows *CORRECTION*1m
YOLO algorithm *CORRECTION*1m
1개 연습문제
Detection algorithms30m
완료하는 데 5시간 필요
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!
완료하는 데 5시간 필요
11개 동영상 (총 76분), 3 readings, 3 quizzes
11개의 동영상
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
3개의 읽기 자료
Triplet Loss *CORRECTION*1m
Face Verification and Binary Classification *CORRECTION*1m
Style Cost *CORRECTION*1m
1개 연습문제
Special applications: Face recognition & Neural style transfer30m
검토
4.9
CONVOLUTIONAL NEURAL NETWORKS의 최상위 리뷰
RS 제공Dec 11, 2019
Great Course Overall\n\nOne 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.
SH 제공Aug 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.
RK 제공Sep 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.
AG 제공Jan 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.
FH 제공Jan 11, 2019
Amazing! Feels like AI is getting tamed in my hands. Course lectures , assignments are excellent. To those who are not well versed with python - numpy and tensorflow , it would be better to brush up.
EB 제공Nov 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.
DM 제공Apr 21, 2019
This is one of the best courses for CNNs. This gives a very deep understanding of the concepts and helps to understand the brains behind the CNNs and their working in application based environments.
DD 제공Feb 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).
deeplearning.ai 정보
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
심층 학습 전문 분야 정보
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.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
