이 강좌에 대하여: 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.
이 수업의 대상: - Learners that took the first two courses of the specialization. The third course is recommended. - Anyone that already has a solid understanding of densely connected neural networks, and wants to learn convolutional neural networks or work with image data.
강사: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
강사: Head Teaching Assistant - Kian Katanforoosh, Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
강사: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| 기본 정보 | Deep Learning Specialization 에서 5의 강좌 4 |
| 레벨 | 중급 |
| 약정 | 4 weeks of study, 4-5 hours/week |
| 언어 | English, 자막: Chinese (Traditional), Korean |
| 하드웨어 요구 사항 | None currently, unless you'd like to download Jupyter Notebooks locally for offline work. |
| 통과 방법 | 강좌를 수료하려면 모든 평가 과제를 통과하십시오. |
| 사용자 등급 |
- 비디오: Edge Detection Example
- 비디오: More Edge Detection
- 비디오: Padding
- 비디오: Strided Convolutions
- 비디오: Convolutions Over Volume
- 비디오: One Layer of a Convolutional Network
- 비디오: Simple Convolutional Network Example
- 비디오: Pooling Layers
- 비디오: CNN Example
- 비디오: Why Convolutions?
- Notebook: Convolutional Model: step by step
- Notebook: Convolutional Model: application
- 비디오: Yann LeCun Interview
- 비디오: Classic Networks
- 비디오: ResNets
- 비디오: Why ResNets Work
- 비디오: Networks in Networks and 1x1 Convolutions
- 비디오: Inception Network Motivation
- 비디오: Inception Network
- 비디오: Using Open-Source Implementation
- 비디오: Transfer Learning
- 비디오: Data Augmentation
- 비디오: State of Computer Vision
- Notebook: Keras Tutorial - The Happy House (not graded)
- Notebook: Residual Networks
- 비디오: Landmark Detection
- 비디오: Object Detection
- 비디오: Convolutional Implementation of Sliding Windows
- 비디오: Bounding Box Predictions
- 비디오: Intersection Over Union
- 비디오: Non-max Suppression
- 비디오: Anchor Boxes
- 비디오: YOLO Algorithm
- 비디오: (Optional) Region Proposals
- Notebook: Car detection with YOLOv2
- 비디오: One Shot Learning
- 비디오: Siamese Network
- 비디오: Triplet Loss
- 비디오: Face Verification and Binary Classification
- 비디오: What is neural style transfer?
- 비디오: What are deep ConvNets learning?
- 비디오: Cost Function
- 비디오: Content Cost Function
- 비디오: Style Cost Function
- 비디오: 1D and 3D Generalizations
- Notebook: Art generation with Neural Style Transfer
- Notebook: Face Recognition for the Happy House
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| 강좌 구입 | |
|---|---|
| 강좌 자료 이용 | 이용 가능 |
| 성적 평가 자료 이용 | 이용 가능 |
| 최종 성적 받기 | 이용 가능 |
| 공유 가능한 강좌 수료증 취득 | 이용 가능 |
Awesome course!
When reading papers and trying to understand, it takes quite a lot of time.
However, this course is very friendly about teaching the core concepts within the papers.
Great Job! Andrew NG!
Very good course!
Slightly more technical but tremendously interesting class.