About this Course
4.8
17,337개의 평가
2,247개의 리뷰
전문분야

다음의 4/5개 강좌

100% 온라인

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유연한 마감

유연한 마감

일정에 따라 마감일을 재설정합니다.
중급 단계

중급 단계

Hours to complete

완료하는 데 약 21시간 필요

권장: 4 weeks of study, 4-5 hours/week...
사용 가능한 언어

영어

자막: 영어, 중국어 (번체자), 중국어 (간체자), 한국어, 일본어

귀하가 습득할 기술

Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network
전문분야

다음의 4/5개 강좌

100% 온라인

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유연한 마감

유연한 마감

일정에 따라 마감일을 재설정합니다.
중급 단계

중급 단계

Hours to complete

완료하는 데 약 21시간 필요

권장: 4 weeks of study, 4-5 hours/week...
사용 가능한 언어

영어

자막: 영어, 중국어 (번체자), 중국어 (간체자), 한국어, 일본어

강의 계획 - 이 강좌에서 배울 내용

1
Hours to complete
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....
Reading
12 videos (Total 140 min), 3 quizzes
Video12개의 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
Quiz1개 연습문제
The basics of ConvNets20m
2
Hours to complete
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. ...
Reading
11 videos (Total 99 min), 2 quizzes
Video11개의 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
Quiz1개 연습문제
Deep convolutional models20m
3
Hours to complete
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....
Reading
10 videos (Total 85 min), 2 quizzes
Video10개의 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
Quiz1개 연습문제
Detection algorithms20m
4
Hours to complete
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!...
Reading
11 videos (Total 76 min), 3 quizzes
Video11개의 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
Quiz1개 연습문제
Special applications: Face recognition & Neural style transfer20m
4.8
2,247개의 리뷰Chevron Right
진로

39%

이 강좌를 수료한 후 새로운 경력 시작하기
경력 혜택

38%

이 강좌를 통해 확실한 경력상 이점 얻기
경력 프로모션

10%

급여 인상 또는 승진하기

최상위 리뷰

대학: AGJan 13th 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.

대학: FHJan 12th 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.

강사

Avatar

Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
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Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
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Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai

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....
심층 학습

자주 묻는 질문

  • 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.

  • 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.

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