このコースについて
4.8
14,850 ratings
1,975 reviews
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....
Stacks

次の専門講座における5コース4

Globe

100%オンラインコース

自分のスケジュールですぐに学習を始めてください。
Calendar

柔軟性のある期限

スケジュールに従って期限をリセットします。
Intermediate Level

中級レベル

Clock

推奨:4 weeks of study, 4-5 hours/week

約19時間で修了
Comment Dots

English

字幕:English, Korean, Chinese (Traditional), Japanese

習得するスキル

Facial Recognition SystemTensorflowConvolutional Neural NetworkArtificial Neural Network
Stacks

次の専門講座における5コース4

Globe

100%オンラインコース

自分のスケジュールですぐに学習を始めてください。
Calendar

柔軟性のある期限

スケジュールに従って期限をリセットします。
Intermediate Level

中級レベル

Clock

推奨:4 weeks of study, 4-5 hours/week

約19時間で修了
Comment Dots

English

字幕:English, Korean, Chinese (Traditional), Japanese

シラバス - 本コースの学習内容

1

セクション
Clock
6時間で修了

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本の動画(合計140分), 3 quizzes
Video12件のビデオ
Edge Detection Example11 分
More Edge Detection7 分
Padding9 分
Strided Convolutions9 分
Convolutions Over Volume10 分
One Layer of a Convolutional Network16 分
Simple Convolutional Network Example8 分
Pooling Layers10 分
CNN Example12 分
Why Convolutions?9 分
Yann LeCun Interview27 分
Quiz1の練習問題
The basics of ConvNets20 分

2

セクション
Clock
5時間で修了

Deep convolutional models: case studies

Learn about the practical tricks and methods used in deep CNNs straight from the research papers. ...
Reading
11本の動画(合計99分), 2 quizzes
Video11件のビデオ
Classic Networks18 分
ResNets7 分
Why ResNets Work9 分
Networks in Networks and 1x1 Convolutions6 分
Inception Network Motivation10 分
Inception Network8 分
Using Open-Source Implementation4 分
Transfer Learning8 分
Data Augmentation9 分
State of Computer Vision12 分
Quiz1の練習問題
Deep convolutional models20 分

3

セクション
Clock
4時間で修了

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本の動画(合計85分), 2 quizzes
Video10件のビデオ
Landmark Detection5 分
Object Detection5 分
Convolutional Implementation of Sliding Windows11 分
Bounding Box Predictions14 分
Intersection Over Union4 分
Non-max Suppression8 分
Anchor Boxes9 分
YOLO Algorithm7 分
(Optional) Region Proposals6 分
Quiz1の練習問題
Detection algorithms20 分

4

セクション
Clock
5時間で修了

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本の動画(合計76分), 3 quizzes
Video11件のビデオ
One Shot Learning4 分
Siamese Network4 分
Triplet Loss15 分
Face Verification and Binary Classification6 分
What is neural style transfer?2 分
What are deep ConvNets learning?7 分
Cost Function3 分
Content Cost Function3 分
Style Cost Function13 分
1D and 3D Generalizations9 分
Quiz1の練習問題
Special applications: Face recognition & Neural style transfer20 分
4.8
Direction Signs

38%

コース終了後に新しいキャリアをスタートした
Briefcase

83%

コースが具体的なキャリアアップにつながった

人気のレビュー

by EBNov 3rd 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.

by DGFeb 14th 2018

Too much hand-holding during assignments, although still very good directions. Obviously the issue with the final programming assignment needs to be addressed. Fantastic lecture material, as always.

講師

Andrew Ng

Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/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.ai

deeplearning.aiについて

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

Deep Learningの専門講座について

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

よくある質問

  • 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.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。