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강의 계획 - 이 강좌에서 배울 내용

콘텐츠 평가Thumbs Up94%(43,086개의 평가)Info
1

1

완료하는 데 6시간 필요

Foundations of Convolutional Neural Networks

완료하는 데 6시간 필요
12개 동영상 (총 140분), 4 개의 읽기 자료, 3 개의 테스트
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
2

2

완료하는 데 5시간 필요

Deep convolutional models: case studies

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11개 동영상 (총 99분), 1 개의 읽기 자료, 2 개의 테스트
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
3

3

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Object detection

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10개 동영상 (총 85분), 2 개의 읽기 자료, 2 개의 테스트
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
4

4

완료하는 데 5시간 필요

Special applications: Face recognition & Neural style transfer

완료하는 데 5시간 필요
11개 동영상 (총 76분), 3 개의 읽기 자료, 3 개의 테스트
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

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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....
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